<?xml version="1.0" encoding="UTF-8"?>
<feed xmlns="http://www.w3.org/2005/Atom">
    <title>data-science</title>
    <link rel="self" type="application/atom+xml" href="https://links.biapy.com/guest/tags/212/feed"/>
    <updated>2026-09-17T19:57:41+00:00</updated>
    <id>https://links.biapy.com/guest/tags/212/feed</id>
            <entry>
            <id>https://links.biapy.com/links/13911</id>
            <title type="text"><![CDATA[Data Science for Beginners - A Curriculum]]></title>
            <link rel="alternate" href="https://github.com/microsoft/data-science-for-beginners" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/13911"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[10 Weeks, 20 Lessons, Data Science for All!

Azure Cloud Advocates at Microsoft are pleased to offer a 10-week, 20-lesson curriculum all about Data Science. Each lesson includes pre-lesson and post-lesson quizzes, written instructions to complete the lesson, a solution, and an assignment. Our project-based pedagogy allows you to learn while building, a proven way for new skills to &amp;#039;stick&amp;#039;.]]>
            </summary>
            <updated>2026-09-14T07:47:23+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/12078</id>
            <title type="text"><![CDATA[Hardwood]]></title>
            <link rel="alternate" href="https://github.com/hardwood-hq/hardwood" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/12078"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[A minimal dependency implementation of Apache Parquet.

A parser for the Apache Parquet file format, optimized for minimal dependencies and great performance.

Related contents:

- [Hardwood: A New Parser for Apache Parquet @ Gunnar Morling](https://www.morling.dev/blog/hardwood-new-parser-for-apache-parquet/).]]>
            </summary>
            <updated>2026-03-11T07:14:06+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/12044</id>
            <title type="text"><![CDATA[Label Studio]]></title>
            <link rel="alternate" href="https://labelstud.io/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/12044"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Open Source Data Labeling.

The most flexible data labeling platform to fine-tune LLMs, prepare training data, or evaluate AI systems.
 Label Studio is a multi-type data labeling and annotation tool with standardized output format.
Label Studio is an open source data labeling tool. It lets you label data types like audio, text, images, videos, and time series with a simple and straightforward UI and export to various model formats. It can be used to prepare raw data or improve existing training data to get more accurate ML models.

- [Label Studio @ GitHub](https://github.com/HumanSignal/label-studio/).]]>
            </summary>
            <updated>2026-03-06T14:56:24+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/11952</id>
            <title type="text"><![CDATA[numpy-ts]]></title>
            <link rel="alternate" href="https://numpyts.dev/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/11952"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Full NumPy, in TypeScript/JavaScript (94% coverage). 

The most comprehensive NumPy implementation for TypeScript and JavaScript. Write numerical computing code with the same API you already know from Python — fully type-safe, tree-shakeable, and validated against NumPy itself.

- [numpy-ts @ GitHub](https://github.com/dupontcyborg/numpy-ts).]]>
            </summary>
            <updated>2026-02-27T12:46:48+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/11916</id>
            <title type="text"><![CDATA[GNU Octave]]></title>
            <link rel="alternate" href="https://octave.org/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/11916"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Scientific Programming Language.

Powerful mathematics-oriented syntax with built-in 2D/3D plotting and visualization tools.
Free software, runs on GNU/Linux, macOS, BSD, and Microsoft Windows.
Drop-in compatible with many Matlab scripts.]]>
            </summary>
            <updated>2026-02-24T15:13:49+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/11775</id>
            <title type="text"><![CDATA[Parquet]]></title>
            <link rel="alternate" href="https://parquet.apache.org/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/11775"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Apache Parquet is an open source, column-oriented data file format designed for efficient data storage and retrieval. It provides high performance compression and encoding schemes to handle complex data in bulk and is supported in many programming languages and analytics tools.

- [Apache Parquet Format @ GitHub](https://github.com/apache/parquet-format).

Related contents:

- [Building Your Modern Data Analytics Stack with Python, Parquet, and DuckDB @ KD nuggets](https://www.kdnuggets.com/building-your-modern-data-analytics-stack-with-python-parquet-and-duckdb).]]>
            </summary>
            <updated>2026-02-11T10:19:02+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/11446</id>
            <title type="text"><![CDATA[Jupyter Docker Stacks]]></title>
            <link rel="alternate" href="https://jupyter-docker-stacks.readthedocs.io/en/latest/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/11446"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Jupyter Docker Stacks are a set of ready-to-run Docker images containing Jupyter applications and interactive computing tools.

- [Jupyter Docker Stacks @ GitHub](https://github.com/jupyter/docker-stacks).
- [Jupyter Docker Stacks @ Quay.io](https://quay.io/organization/jupyter).]]>
            </summary>
            <updated>2026-01-12T15:25:05+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/11421</id>
            <title type="text"><![CDATA[Apache Spark]]></title>
            <link rel="alternate" href="https://spark.apache.org/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/11421"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Unified Engine for large-scale data analytics.

Apache Spark™ is a multi-language engine for executing data engineering, data science, and machine learning on single-node machines or clusters. 

- [Apache Spark @ GitHub](https://github.com/apache/spark).

Related contents:

- [Introducing Apache Spark® 4.1 @ databricks](https://www.databricks.com/blog/introducing-apache-sparkr-41).
- [From Chaos to Scale: Templatizing Spark Declarative Pipelines with DLT-META @ databricks](https://www.databricks.com/blog/chaos-scale-templatizing-spark-declarative-pipelines-dlt-meta).
- [Breaking the Microbatch Barrier: The Architecture of Apache Spark Real-Time Mode @ databricks](https://www.databricks.com/blog/breaking-microbatch-barrier-architecture-apache-spark-real-time-mode).]]>
            </summary>
            <updated>2026-03-17T12:31:57+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/10771</id>
            <title type="text"><![CDATA[JAX]]></title>
            <link rel="alternate" href="https://docs.jax.dev/en/latest/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/10771"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[High performance array computing.

Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more.
JAX provides a familiar NumPy-style API for ease of adoption by researchers and engineers.

- [JAX @ GitHub](https://github.com/jax-ml/jax).

Related contents:

- [How the jax.jit() JIT compiler works in jax-js @ eric makes software](https://ekzhang.substack.com/p/how-the-jaxjit-jit-compiler-works).
- [192 Weeks @ Eric Zhang](https://notes.ekzhang.com/reflections/192-weeks).]]>
            </summary>
            <updated>2025-10-27T13:18:36+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/10697</id>
            <title type="text"><![CDATA[Lance]]></title>
            <link rel="alternate" href="https://lancedb.github.io/lance/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/10697"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Modern columnar data format for ML and LLMs implemented in Rust. Convert from parquet in 2 lines of code for 100x faster random access, vector index, and data versioning. Compatible with Pandas, DuckDB, Polars, Pyarrow, and PyTorch with more integrations coming.. 

Lance is a modern columnar data format optimized for machine learning and AI applications. It efficiently handles diverse multimodal data types while providing high-performance querying and versioning capabilities.

- [Lance @ GitHub](https://github.com/lancedb/lance).

Related contents:

- [Lance takes aim at Parquet in file format joust @ The Register](https://www.theregister.com/2025/10/14/lance_parquet/).]]>
            </summary>
            <updated>2025-10-17T12:01:52+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/10261</id>
            <title type="text"><![CDATA[S3GD]]></title>
            <link rel="alternate" href="https://github.com/WhyPhyLabs/s3gd" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/10261"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[S3GD is a highly optimized, PyTorch-compatible Triton implementation of the Smoothed SignSGD optimizer, meant for reinforcement learning post-training.

Related contents:

- [S3GD Optimizer Algorithm @ WhyPhyLabs](https://whyphy.ai/blog/Aug%202025/08-22-2025-s3gd-blog.md).]]>
            </summary>
            <updated>2025-09-18T06:02:21+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/10095</id>
            <title type="text"><![CDATA[pandas]]></title>
            <link rel="alternate" href="https://pandas.pydata.org/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/10095"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Python Data Analysis Library.

pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool.
 Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more 

- [pandas @ GitHub](https://github.com/pandas-dev/pandas).

Related contents:

- [Leveraging Pandas and SQL Together for Efficient Data Analysis @ KD nuggets](https://www.kdnuggets.com/leveraging-pandas-and-sql-together-for-efficient-data-analysis).
- [I Cleaned a Messy CSV File Using Pandas .  Here’s the Exact Process I Follow Every Time. @ towards data science](https://towardsdatascience.com/i-cleaned-a-messy-csv-file-using-pandas-heres-the-exact-process-i-follow-every-time/).
- [Why learn Pandas? @ Geekuni blog](https://blog.geekuni.com/2026/06/why-learn-pandas.html).]]>
            </summary>
            <updated>2026-06-05T12:31:17+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/10071</id>
            <title type="text"><![CDATA[Odin Programming Language]]></title>
            <link rel="alternate" href="https://odin-lang.org/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/10071"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[The Data-Oriented Language for Sane Software Development.

Odin is a general-purpose programming language with distinct typing, built for high performance, modern systems, and built-in data-oriented data types. The Odin Programming Language, the C alternative for the joy of programming.

- [Odin @ GitHub](https://github.com/odin-lang/Odin).

Related contents:

- [Package Managers are Evil @ gingerBill](https://www.gingerbill.org/article/2025/09/08/package-managers-are-evil/).
- [Odin 1.0 Announcement @ gingerBill&amp;#039;s YouTube](https://www.youtube.com/watch?v=dLPAqXi9In0).]]>
            </summary>
            <updated>2026-07-28T05:40:00+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/85</id>
            <title type="text"><![CDATA[Embedding Atlas]]></title>
            <link rel="alternate" href="https://apple.github.io/embedding-atlas/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/85"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Scalable, Interactive Visualization.
Compute &amp;amp; interactively visualize large embeddings.

 Embedding Atlas is a tool that provides interactive visualizations for large embeddings. It allows you to visualize, cross-filter, and search embeddings and metadata. 

- [Embedding Atlas @ GitHub](https://github.com/apple/embedding-atlas).]]>
            </summary>
            <updated>2025-09-17T19:31:20+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/126</id>
            <title type="text"><![CDATA[Tensorlake]]></title>
            <link rel="alternate" href="https://www.tensorlake.ai/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/126"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Context Engineering for AI Systems.

TensorLake transforms unstructured documents into AI-ready data through Document Ingestion APIs and enables building scalable data processing pipelines with a serverless workflow runtime. The platform handles the complexity of document parsing, data extraction, and workflow orchestration on fully managed infrastructure including GPU acceleration.

- [Tensorlake SDK @ GitHub](https://github.com/tensorlakeai/tensorlake).]]>
            </summary>
            <updated>2025-10-16T09:05:38+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/301</id>
            <title type="text"><![CDATA[Positron]]></title>
            <link rel="alternate" href="https://positron.posit.co/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/301"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Positron, a next-generation data science IDE.

- A free, next-generation data science IDE built by Posit PBC.
- An extensible, polyglot tool for writing code and exploring data.
- A familiar environment for reproducible authoring and publishing.

- [Positron @ GitHub](https://github.com/posit-dev/positron).

Related contents:

- [Episode \#103: \#define: props to astronomer @ Changelog &amp;amp; Friends](https://changelog.com/friends/103).]]>
            </summary>
            <updated>2025-12-31T14:23:16+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/398</id>
            <title type="text"><![CDATA[VisiData]]></title>
            <link rel="alternate" href="https://www.visidata.org/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/398"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Open-source data multitool. Data exploration at your fingertips.

VisiData is an interactive multitool for tabular data. It combines the clarity of a spreadsheet, the efficiency of the terminal, and the power of Python, into a lightweight utility which can handle millions of rows with ease.

- [VisiData @ GitHub](https://github.com/saulpw/visidata).]]>
            </summary>
            <updated>2025-11-04T07:54:26+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/481</id>
            <title type="text"><![CDATA[AI Dataset Generator]]></title>
            <link rel="alternate" href="https://github.com/metabase/dataset-generator" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/481"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Generate realistic datasets for demos, learning, and dashboards. Instantly preview data, export as CSV or SQL, and explore with Metabase.]]>
            </summary>
            <updated>2025-08-28T17:17:59+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/682</id>
            <title type="text"><![CDATA[NumPy]]></title>
            <link rel="alternate" href="https://numpy.org/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/682"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[NumPy is the fundamental package for scientific computing in Python. It is a Python library that provides a multidimensional array object, various derived objects (such as masked arrays and matrices), and an assortment of routines for fast operations on arrays, including mathematical, logical, shape manipulation, sorting, selecting, I/O, discrete Fourier transforms, basic linear algebra, basic statistical operations, random simulation and much more.

- [Numpy @ GitHub](https://github.com/numpy/numpy).

Related contents:

- [I don&amp;#039;t like NumPy @ Dynomight](https://dynomight.net/numpy/).]]>
            </summary>
            <updated>2025-08-28T17:51:10+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/780</id>
            <title type="text"><![CDATA[Hyperparam]]></title>
            <link rel="alternate" href="https://hyperparam.app/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/780"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Look At Your Data 👀.

Data quality is the most important factor in machine learning success. Hyperparam brings exploration and analysis of massive text datasets to the browser.

- [Hyperparam @ GitHub](https://github.com/hyparam).]]>
            </summary>
            <updated>2025-08-28T18:07:21+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/812</id>
            <title type="text"><![CDATA[CocoIndex]]></title>
            <link rel="alternate" href="https://cocoindex.io/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/812"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Extract, Transform, Index Data. Easy and Fresh.
CocoIndex is the world&amp;#039;s first open-source engine that supports both custom transformation logic and incremental updates specialized for data indexing.

 With CocoIndex, users declare the transformation, CocoIndex creates &amp;amp; maintains an index, and keeps the derived index up to date based on source update, with minimal computation and changes. 

- [CocoIndex @ GitHub](https://github.com/cocoindex-io/cocoindex).]]>
            </summary>
            <updated>2025-08-28T18:14:23+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/823</id>
            <title type="text"><![CDATA[Boson]]></title>
            <link rel="alternate" href="https://github.com/bosonstack/boson" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/823"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[A self-contained, lightweight and OOB research platform for modern ML.

Boson is a lightweight, fully containerized, and feature-rich machine learning research platform. It centralizes essential tools to help teams keep projects lean, organized, and reproducible—while reducing overhead and boosting productivity. Think Databricks/Sagemaker but local and free.

Boson enables engineers and researchers to iterate faster without getting bogged down by infrastructure or tooling complexity.]]>
            </summary>
            <updated>2025-08-28T18:14:29+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/953</id>
            <title type="text"><![CDATA[loess-rs]]></title>
            <link rel="alternate" href="https://github.com/joaofig/loess-rs" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/953"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[An implementation of the LOESS / LOWESS algorithm in Rust.

- [LOESS — Smoothing data using local regression @ Data Science Collective&amp;#039;s Medium](https://medium.com/data-science-collective/loess-373d43b03564).]]>
            </summary>
            <updated>2025-08-28T18:36:37+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1099</id>
            <title type="text"><![CDATA[Blosc]]></title>
            <link rel="alternate" href="https://www.blosc.org/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1099"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[As data volumes continue to grow in fields like machine learning and scientific computing, optimizing fundamental operations like matrix multiplication becomes increasingly critical. Blosc2&amp;#039;s chunk-based approach offers a new path to efficiency in these scenarios.

Blosc is a high performance compressor optimized for binary data (i.e. floating point numbers, integers and booleans, although it can handle string data too). It has been designed to transmit data to the processor cache faster than the traditional, non-compressed, direct memory fetch approach via a memcpy() OS call. Blosc main goal is not just to reduce the size of large datasets on-disk or in-memory, but also to accelerate memory-bound computations.

- [C-Blosc2 @ GitHub](https://github.com/Blosc/c-blosc2).

Related contents:

- [Compress Better, Compute Bigger @ ironArray](https://ironarray.io/blog/compute-bigger).]]>
            </summary>
            <updated>2025-08-28T18:59:47+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1275</id>
            <title type="text"><![CDATA[aiopandas]]></title>
            <link rel="alternate" href="https://github.com/telekinesis-inc/aiopandas" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1275"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[🚀 Async-Powered Pandas.

 Lightweight Pandas monkey-patch that adds async support to map, apply, applymap, aggregate, and transform, enabling seamless handling of async functions with controlled max_parallel execution.]]>
            </summary>
            <updated>2025-08-28T19:29:01+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1434</id>
            <title type="text"><![CDATA[Virtual Cell Atlas]]></title>
            <link rel="alternate" href="https://arcinstitute.org/tools/virtualcellatlas" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1434"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[The Arc Virtual Cell Atlas is a collection of high quality, curated, open datasets assembled for the purpose of accelerating the creation of virtual cell models. The Atlas includes both observational and perturbational data from over 300 million cells (and growing).

- [Virtual Cell Atlas @ GitHub](https://github.com/ArcInstitute/arc-virtual-cell-atlas/).]]>
            </summary>
            <updated>2025-08-28T19:55:19+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1439</id>
            <title type="text"><![CDATA[Shiny]]></title>
            <link rel="alternate" href="https://shiny.posit.co/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1439"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Easy web apps for data science without the compromises.
No web development skills required.

- [Shiny @ GitHub](https://github.com/rstudio/shiny).
- [Shiny for Python @ GitHub](https://github.com/posit-dev/py-shiny/).

Related contents:

- [Episode 608: R With Eric Nantz @ Coder Radio](https://coder.show/608).
- [Shiny Developer Series homepage](https://shinydevseries.com/).]]>
            </summary>
            <updated>2025-08-28T19:56:15+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1441</id>
            <title type="text"><![CDATA[rix]]></title>
            <link rel="alternate" href="https://docs.ropensci.org/rix/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1441"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Reproducible Data Science Environments with Nix.

{rix} is an R package that leverages Nix, a package manager focused on reproducible builds. With Nix, you can create project-specific environments with a custom version of R, its packages, and all system dependencies (e.g., GDAL). Nix ensures full reproducibility, which is crucial for research and development projects.

- [rix @ GitHub](https://github.com/ropensci/rix/).

Related contents:

- [Episode 608: R With Eric Nantz @ Coder Radio](https://coder.show/608).]]>
            </summary>
            <updated>2025-08-28T19:56:16+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1442</id>
            <title type="text"><![CDATA[R]]></title>
            <link rel="alternate" href="https://www.r-project.org/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1442"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[The R Project for Statistical Computing.

R is a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS.

Related contents:

- [Episode 608: R With Eric Nantz @ Coder Radio](https://coder.show/608).
- [R Weekly Highlights](https://serve.podhome.fm/r-weekly-highlights).
- [R Podcast](https://r-podcast.org).
- [If all the world were a monorepo @ Julie&amp;#039;s Substack](https://jtibs.substack.com/p/if-all-the-world-were-a-monorepo).
- [Python is not a great language for data science. Part 1: The experience @ Genes, Minds, Machines](https://blog.genesmindsmachines.com/p/python-is-not-a-great-language-for).]]>
            </summary>
            <updated>2025-11-26T13:15:21+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1578</id>
            <title type="text"><![CDATA[Data Formulator]]></title>
            <link rel="alternate" href="https://github.com/microsoft/data-formulator" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1578"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[🪄 Create rich visualizations with AI

Data Formulator is an application from Microsoft Research that uses large language models to transform data, expediting the practice of data visualization.

Data Formulator is an AI-powered tool for analysts to iteratively create rich visualizations. Unlike most chat-based AI tools where users need to describe everything in natural language, Data Formulator combines user interface interactions (UI) and natural language (NL) inputs for easier interaction. This blended approach makes it easier for users to describe their chart designs while delegating data transformation to AI.]]>
            </summary>
            <updated>2025-09-04T09:35:37+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1607</id>
            <title type="text"><![CDATA[Data Engineering Zoomcamp]]></title>
            <link rel="alternate" href="https://github.com/DataTalksClub/data-engineering-zoomcamp" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1607"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[A Free 9-Week Course on Data Engineering Fundamentals.

 Master the fundamentals of data engineering by building an end-to-end data pipeline from scratch. Gain hands-on experience with industry-standard tools and best practices.]]>
            </summary>
            <updated>2025-08-28T20:23:39+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1611</id>
            <title type="text"><![CDATA[Modern-Day Oracles or Bullshit Machines ?]]></title>
            <link rel="alternate" href="https://thebullshitmachines.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1611"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[For better or for worse, LLMs are here to stay. We all read content that they produce online, most of us interact with LLM chatbots, and many of us use them to produce content of our own.

In a series of five- to ten-minute lessons, we will explain what these machines are, how they work, and how to thrive in a world where they are everywhere.

You will learn when these systems can save you a lot of time and effort. You will learn when they are likely to steer you wrong. And you will discover how to see through the hype to tell the difference. ?]]>
            </summary>
            <updated>2025-08-28T20:24:33+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1615</id>
            <title type="text"><![CDATA[AI by Hand ✍️ Exercises in Excel]]></title>
            <link rel="alternate" href="https://github.com/ImagineAILab/ai-by-hand-excel" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1615"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[AI by Hand ✍️ Exercises in Excel]]>
            </summary>
            <updated>2025-08-28T20:25:32+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1757</id>
            <title type="text"><![CDATA[Our World in Data]]></title>
            <link rel="alternate" href="https://ourworldindata.org/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1757"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Research and data to make progress against the world’s largest problems.

To make progress against the pressing problems the world faces, we need to be informed by the best research and data.

Our World in Data makes this knowledge accessible and understandable, to empower those working to build a better world.]]>
            </summary>
            <updated>2025-08-28T20:48:50+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1778</id>
            <title type="text"><![CDATA[Streamlit]]></title>
            <link rel="alternate" href="https://github.com/streamlit/streamlit" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1778"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[A faster way to build and share data apps.
Streamlit turns data scripts into shareable web apps in minutes.
All in pure Python. No front‑end experience required.

Streamlit lets you transform Python scripts into interactive web apps in minutes, instead of weeks. Build dashboards, generate reports, or create chat apps. Once you’ve created an app, you can use our Community Cloud platform to deploy, manage, and share your app.]]>
            </summary>
            <updated>2025-08-28T20:52:50+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1849</id>
            <title type="text"><![CDATA[DataBridge]]></title>
            <link rel="alternate" href="https://databridge.gitbook.io/databridge-docs" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1849"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Multi-modal modular data ingestion and retrieval.

DataBridge is an open source library for natural language search and management of multi-modal data. Get started by installing databridge now!

DataBridge is a powerful document processing and retrieval system designed for building intelligent document-based applications. It provides a robust foundation for semantic search, document processing, and AI-powered document interactions.]]>
            </summary>
            <updated>2025-08-28T21:04:04+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1890</id>
            <title type="text"><![CDATA[DeepSeek-R1]]></title>
            <link rel="alternate" href="https://github.com/deepseek-ai/DeepSeek-R1" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1890"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[We introduce our first-generation reasoning models, DeepSeek-R1-Zero and DeepSeek-R1. DeepSeek-R1-Zero, a model trained via large-scale reinforcement learning (RL) without supervised fine-tuning (SFT) as a preliminary step, demonstrated remarkable performance on reasoning. With RL, DeepSeek-R1-Zero naturally emerged with numerous powerful and interesting reasoning behaviors. However, DeepSeek-R1-Zero encounters challenges such as endless repetition, poor readability, and language mixing. To address these issues and further enhance reasoning performance, we introduce DeepSeek-R1, which incorporates cold-start data before RL.

Related contents:

- [DeepSeek Crushes OpenAI o1 with an MIT-Licensed Model—Developers Are Losing It @ AIM](https://analyticsindiamag.com/ai-news-updates/deepseek-crushes-openai-o1-with-an-mit-licensed-model-developers-are-losing-it/).]]>
            </summary>
            <updated>2025-08-28T21:12:04+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1921</id>
            <title type="text"><![CDATA[Stata]]></title>
            <link rel="alternate" href="https://www.stata.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1921"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Your data tell a story. Explore. Visualize. Model. Make a difference.
Better insight starts with Stata.

Stata is statistical software for data science.]]>
            </summary>
            <updated>2025-08-28T21:16:11+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1938</id>
            <title type="text"><![CDATA[Rowfill]]></title>
            <link rel="alternate" href="https://www.rowfill.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1938"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Open-source document processing platform built for knowledge workers.

Rowfill helps extract, analyze, and process data from complex documents, images, PDFs and more with advanced AI capabilities.

- [Rowfill @ GitHub](https://github.com/harishdeivanayagam/rowfill).]]>
            </summary>
            <updated>2025-08-28T21:20:07+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1949</id>
            <title type="text"><![CDATA[Apache Pinot™]]></title>
            <link rel="alternate" href="https://pinot.apache.org/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1949"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Insights, Unlocked in Real Time.

Apache Pinot™: The real-time analytics open source platform for lightning-fast insights, effortless scaling, and cost-effective data-driven decisions.

- [Apache Pinot @ GitHub](https://github.com/apache/pinot).

Related contents:

- [Serving Millions of Apache Pinot™ Queries with Neutrino @ Uber Blog](https://www.uber.com/en-FR/blog/serving-millions-of-apache-pinot-queries-with-neutrino/?uclick_id=ed80e0fe-d305-48c1-b7e9-ed149ec25b99).]]>
            </summary>
            <updated>2025-08-28T21:21:06+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1970</id>
            <title type="text"><![CDATA[GPU Glossary]]></title>
            <link rel="alternate" href="https://modal.com/gpu-glossary/readme" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1970"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[We wrote this glossary to solve a problem we ran into working with GPUs here at Modal : the documentation is fragmented, making it difficult to connect concepts at different levels of the stack, like Streaming Multiprocessor Architecture , Compute Capability , and nvcc compiler flags .]]>
            </summary>
            <updated>2025-08-28T21:24:22+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1972</id>
            <title type="text"><![CDATA[🚀 E2M]]></title>
            <link rel="alternate" href="https://github.com/wisupai/e2m" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1972"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Everything to Markdown.

 E2M converts various file types (doc, docx, epub, html, htm, url, pdf, ppt, pptx, mp3, m4a) into Markdown. It’s easy to install, with dedicated parsers and converters, supporting custom configs. E2M offers an all-in-one, flexible, and open-source solution.]]>
            </summary>
            <updated>2025-08-28T21:25:09+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1981</id>
            <title type="text"><![CDATA[JAX]]></title>
            <link rel="alternate" href="https://jax.readthedocs.io/en/latest/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1981"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[High performance array computing.

 Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more 

- [JAX @ GitHub](https://github.com/jax-ml/jax).

Related contents:

- [The PyTorch developer&amp;#039;s guide to JAX fundamentals @ Google Cloud Blog](https://cloud.google.com/blog/products/ai-machine-learning/guide-to-jax-for-pytorch-developers).]]>
            </summary>
            <updated>2025-08-28T21:26:10+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1985</id>
            <title type="text"><![CDATA[sitefetch]]></title>
            <link rel="alternate" href="https://github.com/egoist/sitefetch" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1985"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Fetch an entire site and save it as a text file (to be used with AI models).]]>
            </summary>
            <updated>2025-08-28T21:28:12+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2200</id>
            <title type="text"><![CDATA[Zasper]]></title>
            <link rel="alternate" href="https://zasper.io/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2200"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[A Supercharged IDE for Data Science.

Zasper is an IDE designed from the ground up to support massive concurrency. It provides a minimal memory footprint, exceptional speed, and the ability to handle numerous concurrent connections.

It&amp;#039;s perfectly suited for running REPL-style data applications, e.g. Jupyter notebooks. 

- [Zasper @ GitHub](https://github.com/zasper-io/zasper).]]>
            </summary>
            <updated>2025-08-28T22:02:35+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2212</id>
            <title type="text"><![CDATA[Musoq]]></title>
            <link rel="alternate" href="https://puchaczov.github.io/Musoq/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2212"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[SQL-like Querying for Various Data Sources.

Musoq lets you use SQL-like queries on files, directories, images and other data sources without a database. It&amp;#039;s designed to ease life for developers.

Musoq is a tool that lets developers and IT professionals query different data sources using SQL-like syntax, without needing to import data into a database first. It’s designed for scenarios where you need to analyze files, directories, archives, or other data sources quickly and efficiently.

- [Musoq @ GitHub](https://github.com/Puchaczov/Musoq).]]>
            </summary>
            <updated>2025-08-28T22:04:38+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2222</id>
            <title type="text"><![CDATA[SDF Labs]]></title>
            <link rel="alternate" href="https://www.sdf.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2222"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Data Runs Better on SDF. Transform Data Better with SDF.
SDF is the fastest way to build a scalable, reliable, and optimized data warehouse.

SDF is a developer platform for data that scales SQL understanding across an organization, empowering all data teams to unlock the full potential of their data.

SDF is a multi-dialect SQL compiler, transformation framework, and analytical database engine. It natively compiles SQL dialects, like Snowflake, and connects to their corresponding data warehouses to materialize models.

- [SDF Docs](https://docs.sdf.com/).
- [SDF @ GitHub](https://github.com/sdf-labs/sdf-cli).

Source: [Testing is Not Enough: Transforming Data Quality with Write, Audit, Publish using SDF Build @ SDF Blog](https://blog.sdf.com/p/testing-is-not-enough-transforming).]]>
            </summary>
            <updated>2025-08-28T22:06:34+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2223</id>
            <title type="text"><![CDATA[OmniParse]]></title>
            <link rel="alternate" href="https://omniparse.cognitivelab.in/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2223"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Convert Anything into Structured Actionable Data.

 Ingest, parse, and optimize any data format ➡️ from documents to multimedia ➡️ for enhanced compatibility with GenAI frameworks.

OmniParse is a platform that ingests and parses any unstructured data into structured, actionable data optimized for GenAI (LLM) applications. Whether you are working with documents, tables, images, videos, audio files, or web pages, OmniParse prepares your data to be clean, structured, and ready for AI applications such as RAG, fine-tuning, and more

-  [OmniParse @ GitHub](https://github.com/adithya-s-k/omniparse).]]>
            </summary>
            <updated>2025-08-28T22:06:35+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2270</id>
            <title type="text"><![CDATA[OpenVINO]]></title>
            <link rel="alternate" href="https://docs.openvino.ai/2024/index.html" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2270"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference.

OpenVINO is an open-source toolkit for optimizing and deploying deep learning models from cloud to edge. It accelerates deep learning inference across various use cases, such as generative AI, video, audio, and language with models from popular frameworks like PyTorch, TensorFlow, ONNX, and more. Convert and optimize models, and deploy across a mix of Intel® hardware and environments, on-premises and on-device, in the browser or in the cloud.

- [OpenVINO @ GitHub](https://github.com/openvinotoolkit/openvino).
- [OpenVINO @ Hugging Face](https://huggingface.co/OpenVINO).]]>
            </summary>
            <updated>2026-01-23T10:38:15+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2277</id>
            <title type="text"><![CDATA[GPU Puzzles]]></title>
            <link rel="alternate" href="https://github.com/srush/GPU-Puzzles" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2277"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Solve puzzles. Learn CUDA.

GPU architectures are critical to machine learning, and seem to be becoming even more important every day. However, you can be an expert in machine learning without ever touching GPU code. It is hard to gain intuition working through abstractions.

This notebook is an attempt to teach beginner GPU programming in a completely interactive fashion. Instead of providing text with concepts, it throws you right into coding and building GPU kernels. The exercises use NUMBA which directly maps Python code to CUDA kernels. It looks like Python but is basically identical to writing low-level CUDA code. In a few hours, I think you can go from basics to understanding the real algorithms that power 99% of deep learning today. If you do want to read the manual, it is here:]]>
            </summary>
            <updated>2025-08-28T22:16:40+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2278</id>
            <title type="text"><![CDATA[Metal Puzzles]]></title>
            <link rel="alternate" href="https://github.com/abeleinin/Metal-Puzzles" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2278"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Solve Puzzles. Learn Metal 🤘.

Port of srush/GPU-Puzzles to Metal using MLX Custom Kernals. 

GPUs are crucial in machine learning because they can process data on a massively parallel scale. While it&amp;#039;s possible to become an expert in machine learning without writing any GPU code, building intuition is challenging when you&amp;#039;re only working through layers of abstraction. Additionally, as models grow in complexity, the need for developers to write efficient, high-performance kernels becomes increasingly important to leverage the power of modern hardware.]]>
            </summary>
            <updated>2025-08-28T22:16:41+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2586</id>
            <title type="text"><![CDATA[Apache Arrow]]></title>
            <link rel="alternate" href="https://arrow.apache.org/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2586"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[The universal columnar format and multi-language toolbox for fast data interchange and in-memory analytics.

Apache Arrow defines a language-independent columnar memory format for flat and hierarchical data, organized for efficient analytic operations on modern hardware like CPUs and GPUs. The Arrow memory format also supports zero-copy reads for lightning-fast data access without serialization overhead.

- [Apache Arrow @ GitHub](https://github.com/apache/arrow/).
- [arrow-rs @ GitHub](https://github.com/apache/arrow-rs).

Related contents:

- [Fast columnar JSON decoding with arrow-rs @ arroyo](https://www.arroyo.dev/blog/fast-arrow-json-decoding).
- [I spent 6 hours learning Apache Arrow: Overview @ Data Engineer Things&amp;#039;s Medium](https://blog.det.life/i-spent-6-hours-learning-apache-arrow-overview-e7f3b8ee85b2).]]>
            </summary>
            <updated>2025-08-28T23:07:06+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2593</id>
            <title type="text"><![CDATA[Apache Iceberg™]]></title>
            <link rel="alternate" href="https://iceberg.apache.org/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2593"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[The open table format for analytic datasets.

Iceberg is a high-performance format for huge analytic tables. Iceberg brings the reliability and simplicity of SQL tables to big data, while making it possible for engines like Spark, Trino, Flink, Presto, Hive and Impala to safely work with the same tables, at the same time.

- [Apache Iceberg @ GitHub](https://github.com/apache/iceberg).

Related contents:

- [PyIceberg: Current State and Roadmap @ Ju Data Engineering Newsletter](https://juhache.substack.com/p/pyiceberg-current-state-and-roadmap).
- [The Equality Delete Problem in Apache Iceberg @ Data Engineer Things&amp;#039;s Medium](https://blog.dataengineerthings.org/the-equality-delete-problem-in-apache-iceberg-143dd451a974).
- [How I Saved Millions by Restructuring Iceberg Metadata @ Gautham Gondi&amp;#039;s Medium](https://medium.com/@gauthamnagendra/how-i-saved-millions-by-restructuring-iceberg-metadata-c4f5c1de69c2).
- [High Throughput Ingestion with Iceberg @ Adobe Tech Blog&amp;#039;s Medium](https://medium.com/adobetech/high-throughput-ingestion-with-iceberg-ccf7877a413f).
- [Scaling Iceberg Writes with Confidence: A Conflict-Free Distributed Architecture for Fast, Concurrent, Consistent Append-Only Writes @ e6data](https://www.e6data.com/blog/iceberg-distributed-architecture-fast-concurrent-append-writes).
- [Postgres Is the Gateway Drug @ Vignesh Ravichandran](https://viggy28.dev/article/postgres-gateway-drug/).]]>
            </summary>
            <updated>2026-03-23T16:38:12+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2612</id>
            <title type="text"><![CDATA[KlongPy]]></title>
            <link rel="alternate" href="https://github.com/briangu/klongpy" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2612"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[High-Performance Klong array language in Python.

KlongPy is a Python adaptation of the Klong array language, known for its high-performance vectorized operations that leverage the power of NumPy. Embracing a &amp;quot;batteries included&amp;quot; philosophy, KlongPy combines built-in modules with Python&amp;#039;s expansive ecosystem, facilitating rapid application development with Klong&amp;#039;s succinct syntax.]]>
            </summary>
            <updated>2025-08-28T23:13:07+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2706</id>
            <title type="text"><![CDATA[Polars]]></title>
            <link rel="alternate" href="https://pola.rs/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2706"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[DataFrames for the new era.

Dataframes powered by a multithreaded, vectorized query engine, written in Rust 

Polars is a DataFrame interface on top of an OLAP Query Engine implemented in Rust using Apache Arrow Columnar Format as the memory model.

Polars is an open-source library for data manipulation, known for being one of the fastest data processing solutions on a single machine. It features a well-structured, typed API that is both expressive and easy to use.

- [Polars @ GitHub](https://github.com/pola-rs/polars/).

Related contents:

- [Polars at Decathlon: Ready to Play? @ Decathlon Digital](https://medium.com/decathlondigital/polars-at-decathlon-ready-to-play-6abc4328d06c).
- [Decathlon Switches to Polars to Optimize Data Pipelines and Infrastructure Costs @ InfoQ](https://www.infoq.com/news/2025/12/decathlon-spark-polars/).
- [DuckDB beats Polars for 1TB of data @ Confessions of a Data Guy](https://www.confessionsofadataguy.com/duckdb-beats-polars-for-1tb-of-data/).]]>
            </summary>
            <updated>2026-01-15T07:47:37+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2727</id>
            <title type="text"><![CDATA[The Data Engineering Handbook]]></title>
            <link rel="alternate" href="https://github.com/DataExpert-io/data-engineer-handbook" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2727"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[This repo has all the resources you need to become an amazing data engineer!]]>
            </summary>
            <updated>2025-08-28T23:31:16+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2803</id>
            <title type="text"><![CDATA[Monte Carlo]]></title>
            <link rel="alternate" href="https://www.montecarlodata.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2803"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Data and AI reliability. Delivered.

Data breaks. Monte Carlo ensures your team is the first to know and solve with end-to-end data observability.

- [Continuous Compliance Monitoring @ Mike Carpenter&amp;#039;s Medium](https://medium.com/mission-lane-tech-blog/continuous-compliance-monitoring-a4895502e1b2).]]>
            </summary>
            <updated>2025-08-28T23:43:27+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2853</id>
            <title type="text"><![CDATA[Databricks]]></title>
            <link rel="alternate" href="https://www.databricks.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2853"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[The Databricks Data Intelligence Platform.
Databricks brings AI to your data to help you bring AI to the world.

Related contents:

- [SQL Gets Easier: Announcing New Pipe Syntax @ Databricks blog](https://www.databricks.com/blog/sql-gets-easier-announcing-new-pipe-syntax).]]>
            </summary>
            <updated>2025-08-28T23:53:28+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2884</id>
            <title type="text"><![CDATA[PandasAI]]></title>
            <link rel="alternate" href="https://pandas-ai.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2884"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Conversational Data Analysis.

PandasAI is a Python platform that makes it easy to ask questions to your data in natural language. It helps non-technical users to interact with their data in a more natural way, and it helps technical users to save time, and effort when working with data.

PandasAI is a Python library that integrates generative artificial intelligence capabilities into pandas, making dataframes conversational.
Chat with your database (SQL, CSV, pandas, polars, mongodb, noSQL, etc). PandasAI makes data analysis conversational using LLMs (GPT 3.5 / 4, Anthropic, VertexAI) and RAG. 

- [PandasAI @ GitHub](https://github.com/Sinaptik-AI/pandas-ai).]]>
            </summary>
            <updated>2025-08-28T23:57:35+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2939</id>
            <title type="text"><![CDATA[Substrait]]></title>
            <link rel="alternate" href="https://substrait.io/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2939"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Cross-Language Serialization for Relational Algebra.
 A cross platform way to express data transformation, relational algebra, standardized record expression and plans. 

Substrait is a format for describing compute operations on structured data. It is designed for interoperability across different languages and systems.

- [Substrait @ GitHub](https://github.com/substrait-io/substrait).
- [Your November Dose of Data - November 2024 @ Data Council](https://mailchi.mp/datacouncil/october-6416684).]]>
            </summary>
            <updated>2025-08-29T00:07:18+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2941</id>
            <title type="text"><![CDATA[Dagster]]></title>
            <link rel="alternate" href="https://dagster.io/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2941"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Cloud-native orchestration of data pipelines. Ship data pipelines with extraordinary velocity.
An orchestration platform for the development, production, and observation of data assets. 

The cloud-native orchestrator for the whole development lifecycle, with integrated lineage and observability, a declarative programming model, and best-in-class testability.

Dagster is a cloud-native data pipeline orchestrator for the whole development lifecycle, with integrated lineage and observability, a declarative programming model, and best-in-class testability.

It is designed for developing and maintaining data assets, such as tables, data sets, machine learning models, and reports.

- [Dagster @ GitHub](https://github.com/dagster-io/dagster).]]>
            </summary>
            <updated>2025-08-29T00:07:19+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2942</id>
            <title type="text"><![CDATA[OpenMetadata]]></title>
            <link rel="alternate" href="https://open-metadata.org/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2942"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Open and unified metadata platform for data discovery, observability, and governance.

A single place for all your data and all your data practitioners to build and manage high quality data assets at scale. Built by Collate and the founders of Apache Hadoop, Apache Atlas, and Uber Databook.

OpenMetadata is a unified metadata platform for data discovery, data observability, and data governance powered by a central metadata repository, in-depth column level lineage, and seamless team collaboration. 

OpenMetadata is a unified metadata platform for data discovery, data observability, and data governance powered by a central metadata repository, in-depth column level lineage, and seamless team collaboration. It is one of the fastest-growing open-source projects with a vibrant community and adoption by a diverse set of companies in a variety of industry verticals. Based on Open Metadata Standards and APIs, supporting connectors to a wide range of data services, OpenMetadata enables end-to-end metadata management, giving you the freedom to unlock the value of your data assets.

- [OpenMetadata @ GitHub](https://github.com/open-metadata/OpenMetadata).]]>
            </summary>
            <updated>2025-08-29T00:07:20+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2943</id>
            <title type="text"><![CDATA[data stack in a box]]></title>
            <link rel="alternate" href="https://github.com/wisemuffin/nsw-doe-data-stack-in-a-box" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2943"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Department of Education (DOE) for New South Wales (AUS) data stack in a box.
With the push of one button you can have your own data stack up and running in 5 mins! 🏎️.

- [Data Stack in a Box — New South Wales Department of Education (ft. DuckDB, Dagster, dbt, dlt and Evidence.dev) @ David Griffiths&amp;#039; Medium](https://davidgriffiths-data.medium.com/data-stack-in-a-box-new-south-wales-department-of-education-ft-e2bd12840d3e).
- [Your November Dose of Data - November 2024 @ Data Council](https://mailchi.mp/datacouncil/october-6416684).]]>
            </summary>
            <updated>2025-08-29T00:07:21+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2949</id>
            <title type="text"><![CDATA[Docling]]></title>
            <link rel="alternate" href="https://docling-project.github.io/docling/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2949"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Docling parses documents and exports them to the desired format with ease and speed.
🗂️ Reads popular document formats (PDF, DOCX, PPTX, Images, HTML, AsciiDoc, Markdown) and exports to Markdown and JSON.

- [Docling @ GitHub](https://github.com/docling-project/docling).

Related contents:

- [Docling - Pour convertir vos documents sans prise de tête @ Korben :fr:](https://korben.info/docling-convertisseur-documents-multi-formats.html).
- [Episode \#125: The state of homelab tech (2026) @ Changelog &amp;amp; Friends](https://changelog.com/friends/125).]]>
            </summary>
            <updated>2026-01-27T07:14:15+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2978</id>
            <title type="text"><![CDATA[DataChain]]></title>
            <link rel="alternate" href="https://datachain.ai/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2978"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[AI Data Management at Scale - Curate, Enrich, and Version Datasets.

DataChain is a modern Pythonic data-frame library designed for artificial intelligence. It is made to organize your unstructured data into datasets and wrangle it at scale on your local machine. Datachain does not abstract or hide the AI models and API calls, but helps to integrate them into the postmodern data stack.

Datachain enables multimodal API calls and local AI inferences to run in parallel over many samples as chained operations. The resulting datasets can be saved, versioned, and sent directly to PyTorch and TensorFlow for training. Datachain can persist features of Python objects returned by AI models, and enables vectorized analytical operations over them.

- [DataChain @ GitHub](https://github.com/iterative/datachain).]]>
            </summary>
            <updated>2025-08-29T00:13:11+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2996</id>
            <title type="text"><![CDATA[CSV SQL Tool]]></title>
            <link rel="alternate" href="https://csvsqltool.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2996"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Run SQL queries on CSV files directly in your browser. No data leaves your browser.
Fast, private, and easy to use.]]>
            </summary>
            <updated>2025-08-29T00:16:18+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3016</id>
            <title type="text"><![CDATA[Clidey WhoDB]]></title>
            <link rel="alternate" href="https://whodb.clidey.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3016"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[A lightweight next-gen data explorer - Postgres, MySQL, SQLite, MongoDB, Redis, MariaDB &amp;amp; Elastic Search with Chat interface.

- [WhoDB @ GitHub](https://github.com/clidey/whodb).]]>
            </summary>
            <updated>2025-08-29T00:20:19+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3052</id>
            <title type="text"><![CDATA[Panel]]></title>
            <link rel="alternate" href="https://panel.holoviz.org/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3052"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[The powerful data exploration &amp;amp; web app framework for Python.

Panel is an open-source Python library designed to streamline the development of robust tools, dashboards, and complex applications entirely within Python. With a comprehensive philosophy, Panel integrates seamlessly with the PyData ecosystem, offering powerful, interactive data tables, visualizations, and much more, to unlock, visualize, share, and collaborate on your data for efficient workflows.

- [Panel @ GitHub](https://github.com/holoviz/panel).]]>
            </summary>
            <updated>2025-08-29T00:25:20+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3054</id>
            <title type="text"><![CDATA[Taipy]]></title>
            <link rel="alternate" href="https://taipy.io/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3054"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Build Python Data &amp;amp; AI web applications.
Turns Data and AI algorithms into production-ready web applications in no time. 

Taipy is designed for data scientists and machine learning engineers to build data &amp;amp; AI web applications.  

From simple pilots to production-ready web applications in no time. No more compromise on performance, customization, and scalability.

- [Taipy @ GitHub](https://github.com/Avaiga/taipy).]]>
            </summary>
            <updated>2025-08-29T00:25:21+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3070</id>
            <title type="text"><![CDATA[Marly AI]]></title>
            <link rel="alternate" href="https://www.marly.ai/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3070"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[The Data Processor for Agents.

Marly allows your agents to extract tables &amp;amp; text from your PDFs, Powerpoints, etc in a structured format making it easy for them to take subsequent actions (database call, API call, creating a chart etc).

- [Marly @ GitHub](https://github.com/marly-ai/marly).]]>
            </summary>
            <updated>2025-08-29T00:28:26+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3100</id>
            <title type="text"><![CDATA[Anyquery]]></title>
            <link rel="alternate" href="https://anyquery.dev/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3100"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Use SQL for everything. Query anything with old-school cool SQL.

Anyquery is a CLI tool to run SQL queries on any data source, no matter if it&amp;#039;s a file, an API, logs, or a local app.
See the integrations for the full extent of what you can do. 

- [Anyquery @ GitHub](https://github.com/julien040/anyquery).]]>
            </summary>
            <updated>2025-08-29T00:33:25+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3166</id>
            <title type="text"><![CDATA[Drasi]]></title>
            <link rel="alternate" href="https://drasi.io/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3166"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Drasi makes it easy and efficient to detect and react to changes in databases.

Drasi is a data processing platform that simplifies detecting changes in data and taking immediate action. It is a comprehensive solution that provides built-in capabilities to track system logs and change feeds for specific events, evaluate them for relevance, and automatically initiate appropriate reactions.

- [Drasi @ GitHub](https://github.com/drasi-project/drasi-platform).

Related contents:

- [Drasi: Microsoft’s newest open-source project simplifies change detection and reaction in complex systems @ Microsoft Blog](https://azure.microsoft.com/en-us/blog/drasi-microsofts-newest-open-source-project-simplifies-change-detection-and-reaction-in-complex-systems/).]]>
            </summary>
            <updated>2025-08-29T00:44:42+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3234</id>
            <title type="text"><![CDATA[Tabled]]></title>
            <link rel="alternate" href="https://github.com/VikParuchuri/tabled" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3234"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Detect and extract tables to markdown and csv.

Tabled is a small library for detecting and extracting tables. It uses surya to find all the tables in a PDF, identifies the rows/columns, and formats cells into markdown, csv, or html.]]>
            </summary>
            <updated>2025-08-29T00:56:52+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3263</id>
            <title type="text"><![CDATA[Vortex]]></title>
            <link rel="alternate" href="https://github.com/spiraldb/vortex" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3263"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[&amp;quot;The LLVM of columnar file formats&amp;quot;. A toolkit for working with compressed Arrow on-disk, in-memory, and over-the-wire. 

Vortex is a toolkit for working with compressed Apache Arrow arrays in-memory, on-disk, and over-the-wire.

Vortex is designed to be to columnar file formats what Apache DataFusion is to query engines (or, analogously, what LLVM + Clang are to compilers): a highly extensible &amp;amp; extremely fast framework for building a modern columnar file format, with a state-of-the-art, &amp;quot;batteries included&amp;quot; reference implementation.]]>
            </summary>
            <updated>2025-08-29T01:00:50+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3307</id>
            <title type="text"><![CDATA[Snowflake]]></title>
            <link rel="alternate" href="https://www.snowflake.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3307"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[The Snowflake AI Data Cloud - Mobilize Data, Apps, and AI.
Snowflake delivers ease of use, instant elasticity, and lower TCO.

- [How to make Product give a shit about your architecture proposal @ Andy G&amp;#039;s Blog](https://gieseanw.wordpress.com/2024/10/09/how-to-make-product-give-a-shit-about-your-architecture-proposal/).]]>
            </summary>
            <updated>2025-08-29T01:08:51+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3316</id>
            <title type="text"><![CDATA[Apache Kylin]]></title>
            <link rel="alternate" href="https://kylin.apache.org/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3316"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Kylin is a high concurrency, high performance and intelligent OLAP engine that provides low-cost and ultimate data analytics experience.

- [Apache Kylin @ GitHub](https://github.com/apache/kylin).
- [#3 Mettre à disposition la donnée lorsque l&amp;#039;on est Data Engineer @ Data-Crafting.io Newsletter :fr:](https://datacrafting.substack.com/p/3-mettre-a-disposition-la-donnee).]]>
            </summary>
            <updated>2025-08-29T01:08:59+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3323</id>
            <title type="text"><![CDATA[sq]]></title>
            <link rel="alternate" href="https://sq.io/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3323"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[wrangle data.

sq is a command line tool that provides jq-style access to structured data sources: SQL databases, or document formats like CSV or Excel. It is the lovechild of sql+jq.

- [sq @ GitHub](https://github.com/neilotoole/sq).]]>
            </summary>
            <updated>2025-08-29T01:10:51+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3463</id>
            <title type="text"><![CDATA[Datasette:]]></title>
            <link rel="alternate" href="https://datasette.io/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3463"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[An open source multi-tool for exploring and publishing data.

Datasette is a tool for exploring and publishing data. It helps people take data of any shape, analyze and explore it, and publish it as an interactive website and accompanying API.

- [Datasette @ GitHub](https://github.com/simonw/datasette).
- [AI tools for software engineers, but without the hype – with Simon Willison (co-creator of Django) @ The Pragmatic Engineer](https://newsletter.pragmaticengineer.com/p/ai-tools-for-software-engineers-simon-willison).]]>
            </summary>
            <updated>2026-08-05T11:55:11+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3478</id>
            <title type="text"><![CDATA[TensorRT SDK]]></title>
            <link rel="alternate" href="https://developer.nvidia.com/tensorrt" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3478"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[NVIDIA® TensorRT™ is an ecosystem of APIs for high-performance deep learning inference. TensorRT includes an inference runtime and model optimizations that deliver low latency and high throughput for production applications. The TensorRT ecosystem includes TensorRT, TensorRT-LLM, TensorRT Model Optimizer, and TensorRT Cloud.

- [TensorRT Open Source Software @ GitHub](https://github.com/NVIDIA/TensorRT).]]>
            </summary>
            <updated>2025-08-29T01:37:16+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3654</id>
            <title type="text"><![CDATA[The Querynomicon]]></title>
            <link rel="alternate" href="https://gvwilson.github.io/sql-tutorial/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3654"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Upon first encountering SQL after two decades of Fortran, C, Java, and Python, I thought I had stumbled into hell. I quickly realized that was optimistic: after all, hell has rules.

I have since realized that SQL does too, and that they are no more confusing or contradictory than those of most other programming languages. They only appear so because it draws on a tradition unfamiliar to those of us raised with derivatives of C. To quote Terry Pratchett, it is not mad, just differently sane.

Welcome, then, to a world in which the strange will become familiar, and the familiar, strange. Welcome, thrice welcome, to SQL.

- [The Querynomicon: An Introduction to SQL for Weary Data Scientists @ GitHub](https://github.com/gvwilson/sql-tutorial).]]>
            </summary>
            <updated>2025-08-29T02:05:44+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3666</id>
            <title type="text"><![CDATA[Firecrawl]]></title>
            <link rel="alternate" href="https://www.firecrawl.dev/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3666"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Turn websites into LLM-ready data.

Power your AI apps with clean data crawled from any website. It&amp;#039;s also open-source.
 🔥 Turn entire websites into LLM-ready markdown or structured data. Scrape, crawl and extract with a single API. 

- [Firecrawl @ GitHub](https://github.com/mendableai/firecrawl).

Related contents:

- [Firecrawl @ GitHub](https://github.com/firecrawl/firecrawl).
- [Firecrawl Observer @ GitHub](https://github.com/firecrawl/firecrawl-observer).

Related contents:

- [🚨 Someone built a tool that turns any website into clean data your AI can actually use @ Nav Toor&amp;#039;s X](https://nitter.net/heynavtoor/status/2031626457110425760).
- [Hermes Agent : veille technique auto-hébergée avec Matrix, FreshRSS et Firecrawl @ Cryptolab :fr:](https://cryptolab.re/posts/2026/hermes-agent-framework-self-hosted/).]]>
            </summary>
            <updated>2026-06-23T05:55:15+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3683</id>
            <title type="text"><![CDATA[ArcticDB]]></title>
            <link rel="alternate" href="https://arcticdb.io/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3683"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[ArcticDB is a  DataFrame Database.

ArcticDB is a high performance, serverless DataFrame database built for the Python Data Science ecosystem. 
Built for the modern Python Data Science ecosystem, ArcticDB transforms your ability to handle complex real world data with Incredibly fast proven Petabyte scale.

- [ArticDB @ GitHub](https://github.com/man-group/ArcticDB).
- [La veille des Ours n°32 @ Bearstech&amp;#039;s LinkedIn :fr:](https://www.linkedin.com/pulse/la-veille-des-ours-n32-bearstech-adpzf/).]]>
            </summary>
            <updated>2025-08-29T02:10:22+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3739</id>
            <title type="text"><![CDATA[Data For Good]]></title>
            <link rel="alternate" href="https://dataforgood.fr/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3739"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Les technologies numériques sont incroyablement puissantes et redéfinissent le fonctionnement de notre société. Pour les acteurs qui œuvrent pour l&amp;#039;intérêt général, la technologie peut parfois être un levier démutiplicateur d&amp;#039;impacts positifs, cependant et malheureusement ces acteurs n&amp;#039;ont souvent pas les ressources technologiques ou humaines pour accélérer leur action citoyenne. Data for Good existe pour rétablir l&amp;#039;équilibre.

- [286 - Data &amp;amp; Dev - Christophe Blefari @ &amp;lt;ifttd&amp;gt; :fr:](https://www.ifttd.io/episodes/data-dev).
- [289 - Data 4 Good - Ronan Sy @ &amp;lt;ifttd&amp;gt; :fr:](https://www.ifttd.io/episodes/data-4-good).]]>
            </summary>
            <updated>2025-08-29T02:19:28+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3741</id>
            <title type="text"><![CDATA[Amazon Athena]]></title>
            <link rel="alternate" href="https://aws.amazon.com/athena/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3741"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Interactive SQL. Analyze petabyte-scale data where it lives with ease and flexibility.

Amazon Athena is a serverless, interactive analytics service built on open-source frameworks, supporting open-table and file formats. Athena provides a simplified, flexible way to analyze petabytes of data where it lives. Analyze data or build applications from an Amazon Simple Storage Service (S3) data lake and 30 data sources, including on-premises data sources or other cloud systems using SQL or Python. Athena is built on open-source Trino and Presto engines and Apache Spark frameworks, with no provisioning or configuration effort required.

- [286 - Data &amp;amp; Dev - Christophe Blefari @ &amp;lt;ifttd&amp;gt; :fr:](https://www.ifttd.io/episodes/data-dev).]]>
            </summary>
            <updated>2025-08-29T02:21:28+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3841</id>
            <title type="text"><![CDATA[Verba]]></title>
            <link rel="alternate" href="https://github.com/weaviate/Verba" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3841"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Retrieval Augmented Generation (RAG) chatbot powered by Weaviate.

Welcome to Verba: The Golden RAGtriever, an open-source application designed to offer an end-to-end, streamlined, and user-friendly interface for Retrieval-Augmented Generation (RAG) out of the box. In just a few easy steps, explore your datasets and extract insights with ease, either locally with HuggingFace and Ollama or through LLM providers such as OpenAI, Cohere, and Google.]]>
            </summary>
            <updated>2025-08-29T02:37:38+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4051</id>
            <title type="text"><![CDATA[Cube]]></title>
            <link rel="alternate" href="https://cube.dev/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4051"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[📊 Cube — The Semantic Layer for Building Data Applications.
The Universal Semantic Layer.

Build trust with a semantic layer. Connect siloed data, define consistent metrics, and power AI and analytics with context.

Cube is the semantic layer for building data applications. It helps data engineers and application developers access data from modern data stores, organize it into consistent definitions, and deliver it to every application.

Cube was designed to work with all SQL-enabled data sources, including cloud data warehouses like Snowflake or Google BigQuery, query engines like Presto or Amazon Athena, and application databases like Postgres. Cube has a built-in relational caching engine to provide sub-second latency and high concurrency for API requests.

- [Cube @ GitHub](https://github.com/cube-js/cube).
- [\#264.exe - GraphQL: Votre nouveau BFF par Christophe Blefari @ &amp;lt;ifttd&amp;gt;](https://www.ifttd.io/episodes/graphql#w-tabs-0-data-w-pane-3).
- [le semantic layer va vous plaire @ Florent J.&amp;#039;s LinkedIn :fr:](https://www.linkedin.com/posts/florent-j-93716410b_vous-voulez-que-nimporte-qui-dans-activity-7152555059996729345-Xh5X/).]]>
            </summary>
            <updated>2025-08-29T03:11:56+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4057</id>
            <title type="text"><![CDATA[Kuzzle]]></title>
            <link rel="alternate" href="https://kuzzle.io/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4057"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[IoT &amp;amp; Data Science Platform, Platform as-a-Service, Kuzzle PaaS. Activate the power of the Kuzzle IoT platform online, with no commitment.

Kuzzle is a generic backend offering the basic building blocks common to every application.

Open-source Back-end, self-hostable &amp;amp; ready to use - Real-time, storage, advanced search - Web, Apps, Mobile, IoT - 

- [Kuzzle @ GitHub](https://github.com/kuzzleio/kuzzle).]]>
            </summary>
            <updated>2025-08-29T03:13:58+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4275</id>
            <title type="text"><![CDATA[Data Version Control · DVC]]></title>
            <link rel="alternate" href="https://dvc.org/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4275"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[🦉 ML Experiments and Data Management with Git 

Data Version Control or DVC is a command line tool and VS Code Extension to help you develop reproducible machine learning projects:

- [Data Version Control @ GitHub](https://github.com/iterative/dvc).
- [270 - DB Vectorielle - Noé Achache @ &amp;lt;ifttd&amp;gt; :fr:](https://www.ifttd.io/episodes/db-vectorielle).]]>
            </summary>
            <updated>2025-08-29T03:50:16+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4289</id>
            <title type="text"><![CDATA[Seaborn]]></title>
            <link rel="alternate" href="https://seaborn.pydata.org/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4289"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[statistical data visualization.

Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.

- [Seaborn @ GitHub](https://github.com/mwaskom/seaborn).
- [La veille des Ours n°31 @ Bearstech&amp;#039;s LinkedIn :fr:](https://www.linkedin.com/pulse/la-veille-des-ours-n31-bearstech-gbmgf/).]]>
            </summary>
            <updated>2025-08-29T03:51:16+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4323</id>
            <title type="text"><![CDATA[dbt]]></title>
            <link rel="alternate" href="https://www.getdbt.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4323"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Transform Data in Your Warehouse. Build trusted data products faster.

Accelerate your data transformation process with dbt Cloud and start delivering data that you and your team can rely on.  dbt enables data analysts and engineers to transform their data using the same practices that software engineers use to build applications. Analysts using dbt can transform their data by simply writing select statements, while dbt handles turning these statements into tables and views in a data warehouse.

- [dbt Core @ GitHub](https://github.com/dbt-labs/dbt-core).
- [dbt Developer Hub](https://docs.getdbt.com/).

Sources:

- [268 - Résilience de la data - Sammy Teillet @ &amp;lt;ifttd&amp;gt; :fr:](https://www.ifttd.io/episodes/resilience-de-la-data).
- [Optimizing SQL queries for speed with dbt @ DataDuel.co](https://www.dataduel.co/optimizing-sql-queries-for-speed-with-dbt/).
- [Test Driven Development (TDD) with dbt: Test First, SQL Later @ Xebia](https://xebia.com/blog/test-driven-development-tdd-with-dbt/).
- [Understanding dbt: basics and best practices @ Datadog](https://www.datadoghq.com/blog/understanding-dbt/).]]>
            </summary>
            <updated>2025-09-08T13:29:23+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4324</id>
            <title type="text"><![CDATA[Metaplane]]></title>
            <link rel="alternate" href="https://www.metaplane.dev/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4324"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Data Observability Platform for Modern Data Teams. Trust the data that powers your business.

Automated end-to-end data observability — so data teams are the first to know about data issues.

- [268 - Résilience de la data - Sammy Teillet @ &amp;lt;ifttd&amp;gt; :fr:](https://www.ifttd.io/episodes/resilience-de-la-data).]]>
            </summary>
            <updated>2025-08-29T03:58:20+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4559</id>
            <title type="text"><![CDATA[Apache Beam®]]></title>
            <link rel="alternate" href="https://beam.apache.org/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4559"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[The Unified Apache Beam Model. The easiest way to do batch and streaming data processing. Write once, run anywhere data processing for mission-critical production workloads.

Apache Beam is a unified programming model for Batch and Streaming data processing.
Apache Beam is a unified model for defining both batch and streaming data-parallel processing pipelines, as well as a set of language-specific SDKs for constructing pipelines and Runners for executing them on distributed processing backends, including Apache Flink, Apache Spark, Google Cloud Dataflow, and Hazelcast Jet. 

- [Beam @ GitHub](https://github.com/apache/beam).]]>
            </summary>
            <updated>2025-08-29T04:36:39+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4560</id>
            <title type="text"><![CDATA[Dataflow]]></title>
            <link rel="alternate" href="https://cloud.google.com/dataflow/?hl=en" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4560"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Unified stream and batch data processing that&amp;#039;s serverless, fast, and cost-effective.

- [ &amp;quot;CI/CD avec Dataflow dans Google Cloud&amp;quot; au GDG Cloud Nantes @ GDG France&amp;#039;s YouTube :fr: ](https://www.youtube.com/watch?v=BK88_bIoCpc).]]>
            </summary>
            <updated>2025-08-29T04:36:39+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4593</id>
            <title type="text"><![CDATA[Taipy]]></title>
            <link rel="alternate" href="https://www.taipy.io/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4593"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Turns Data and AI algorithms into production-ready web applications in no time.  Taipy is an open-source Python library for building production-ready front-end &amp;amp; back-end in no time.

Taipy is an open-source Python library for easy, end-to-end application development,
featuring what-if analyses, smart pipeline execution, built-in scheduling, and deployment tools. 

- [Taipy @ GitHub](https://github.com/Avaiga/taipy).
- [ Streamlit is getting outdated @ LinkedIn&amp;#039;s Python Developers Community](https://www.linkedin.com/feed/update/urn:li:activity:7158773041328508930).]]>
            </summary>
            <updated>2025-08-29T04:42:45+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4820</id>
            <title type="text"><![CDATA[Grafbase]]></title>
            <link rel="alternate" href="https://grafbase.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4820"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[The unified data layer

Connect your APIs, databases and microservices to a unified API at the edge. Delight your users with fast response times globally. Deploy globally fast GraphQL APIs with a top-notch developer experience.

- [Grafbase @ GitHub](https://github.com/grafbase/grafbase).]]>
            </summary>
            <updated>2025-08-29T05:20:01+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4827</id>
            <title type="text"><![CDATA[Lantern]]></title>
            <link rel="alternate" href="https://lantern.dev/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4827"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[The most powerful vector database for building AI applications. Open-source PostgreSQL database extension for vector data and vector search operations.

Lantern is an open-source PostgreSQL database extension to store vector data, generate embeddings, and handle vector search operations.

- [Lantern @ GitHub](https://github.com/lanterndata/lantern).]]>
            </summary>
            <updated>2025-08-29T05:21:04+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4877</id>
            <title type="text"><![CDATA[The Grand Complete Data Science Guide With Videos And Materials]]></title>
            <link rel="alternate" href="https://github.com/krishnaik06/The-Grand-Complete-Data-Science-Materials" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4877"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Contribute to krishnaik06/The-Grand-Complete-Data-Science-Materials development by creating an account on GitHub.]]>
            </summary>
            <updated>2025-08-29T05:31:07+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4897</id>
            <title type="text"><![CDATA[GlareDB]]></title>
            <link rel="alternate" href="https://glaredb.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4897"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Your Data Pipeline, Simplified.  GlareDB: An analytics DBMS for distributed data.

Data exists everywhere: your laptop, Postgres, Snowflake and as files in S3. It exists in various formats such as Parquet, CSV and JSON. Regardless, there will always be multiple steps spanning several destinations to get the insights you need.

GlareDB is designed to query your data wherever it lives using SQL that you already know.

- [GlareDB @ GitHub](https://github.com/GlareDB/glaredb)
- [What the Heck is GlareDB? @ HackerNoon](https://hackernoon.com/what-the-heck-is-glaredb).]]>
            </summary>
            <updated>2025-08-29T05:33:08+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4950</id>
            <title type="text"><![CDATA[Dolt]]></title>
            <link rel="alternate" href="https://www.dolthub.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4950"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Dolt is Git for data. The world&amp;#039;s first and only version-controlled SQL database.

Dolt is a SQL database that you can fork, clone, branch, merge, push and pull just like a Git repository.

Connect to Dolt just like any MySQL database to read or modify schema and data. Version control functionality is exposed in SQL via system tables, functions, and procedures.

- [Dolt @ GitHub](https://github.com/dolthub/dolt).

Related contents:

- [Hunting a Memory Leak @ DoltHub]().]]>
            </summary>
            <updated>2025-08-29T05:41:26+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4956</id>
            <title type="text"><![CDATA[trdsql]]></title>
            <link rel="alternate" href="https://noborus.github.io/trdsql/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4956"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[CLI tool that can execute SQL queries on CSV, LTSV, JSON and TBLN. Can output to various formats.

- [trdsql @ GitHub](https://github.com/noborus/trdsql).]]>
            </summary>
            <updated>2025-11-04T07:56:31+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4959</id>
            <title type="text"><![CDATA[Folium]]></title>
            <link rel="alternate" href="https://python-visualization.github.io/folium/latest/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4959"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Folium builds on the data wrangling strengths of the Python ecosystem and the mapping strengths of the Leaflet.js library. Manipulate your data in Python, then visualize it in a Leaflet map via Folium.

[Folium @ GitHub](https://github.com/python-visualization/folium)]]>
            </summary>
            <updated>2025-08-29T05:43:18+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5102</id>
            <title type="text"><![CDATA[Moses]]></title>
            <link rel="alternate" href="http://www2.statmt.org/moses/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5102"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Moses, the machine translation system.

Moses is a statistical machine translation system that allows you to automatically train translation models for any language pair. All you need is a collection of translated texts (parallel corpus). Once you have a trained model, an efficient search algorithm quickly finds the highest probability translation among the exponential number of choices. 

[Moses @ GitHub](https://github.com/moses-smt/mosesdecoder).]]>
            </summary>
            <updated>2025-08-29T06:07:29+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5103</id>
            <title type="text"><![CDATA[text2vec]]></title>
            <link rel="alternate" href="https://text2vec.org/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5103"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[text2vec is an R package which provides an efficient framework with a concise API for text analysis and natural language processing (NLP).

[text2vec @ GitHub](https://github.com/dselivanov/text2vec).]]>
            </summary>
            <updated>2025-08-29T06:07:29+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5104</id>
            <title type="text"><![CDATA[MITIE]]></title>
            <link rel="alternate" href="https://github.com/mit-nlp/MITIE" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5104"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[library and tools for information extraction.

This project provides free (even for commercial use) state-of-the-art information extraction tools. The current release includes tools for performing named entity extraction and binary relation detection as well as tools for training custom extractors and relation detectors.]]>
            </summary>
            <updated>2025-08-29T06:07:30+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5109</id>
            <title type="text"><![CDATA[RapidMiner]]></title>
            <link rel="alternate" href="https://rapidminer.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5109"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Amplify the Impact of Your People, Expertise &amp;amp; Data.

Altair and RapidMiner share the same vision to make data analytics simple enough for all users, but scalable, governed, and safe enough for all enterprises. RapidMiner is the enterprise-ready data science platform that amplifies the collective impact of your people, expertise and data for breakthrough competitive advantage.]]>
            </summary>
            <updated>2025-08-29T06:08:27+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5110</id>
            <title type="text"><![CDATA[KNIME]]></title>
            <link rel="alternate" href="https://www.knime.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5110"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[KNIME offers a complete platform for end-to-end data science, from creating analytic models, to deploying them and sharing insights within the organization, through to data apps and services.

[KNIME @ GitHub](https://github.com/knime)]]>
            </summary>
            <updated>2025-08-29T06:08:27+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5135</id>
            <title type="text"><![CDATA[MOA]]></title>
            <link rel="alternate" href="https://moa.cms.waikato.ac.nz/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5135"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[MOA is the most popular open source framework for data stream mining,]]>
            </summary>
            <updated>2025-08-29T06:12:30+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5194</id>
            <title type="text"><![CDATA[KNIME Analytics Platform]]></title>
            <link rel="alternate" href="https://www.knime.com/knime-analytics-platform" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5194"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[KNIME Analytics Platform is free and open source, which ensures users remain on the bleeding edge of data science, 300+ connectors to data sources, and integrations to all popular machine learning libraries.]]>
            </summary>
            <updated>2025-08-29T06:23:35+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5197</id>
            <title type="text"><![CDATA[Overture Maps Foundation]]></title>
            <link rel="alternate" href="https://overturemaps.org/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5197"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Powering current and next-generation map products by creating reliable, easy-to-use, and interoperable open map data.

Overture aims to incorporate map data from multiple sources including Overture Members, civic organizations, and open data sources.

Overture is for developers who build map services or use geospatial data.

- [Overture Maps @ GitHub](https://github.com/OvertureMaps).]]>
            </summary>
            <updated>2025-08-29T06:23:38+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5259</id>
            <title type="text"><![CDATA[DataOps.live]]></title>
            <link rel="alternate" href="https://www.dataops.live/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5259"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Data products done right!

DataOps.live is the leading provider of Snowflake environment management, end-to-end orchestration, CI/CD, automated testing &amp;amp; observability, and code management, wrapped in an elegant developer interface.]]>
            </summary>
            <updated>2025-08-29T06:33:38+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5260</id>
            <title type="text"><![CDATA[dbt]]></title>
            <link rel="alternate" href="https://www.getdbt.com/product/what-is-dbt/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5260"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[dbt™ is a SQL-first transformation workflow that lets teams quickly and collaboratively deploy analytics code following software engineering best practices like modularity, portability, CI/CD, and documentation. Now anyone on the data team can safely contribute to production-grade data pipelines.

[dbt @ GitHub](https://github.com/dbt-labs/dbt-core).]]>
            </summary>
            <updated>2025-08-29T06:33:39+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5330</id>
            <title type="text"><![CDATA[SQLPage]]></title>
            <link rel="alternate" href="https://sql.ophir.dev/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5330"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Open-source low-code web application framework. Create full websites writing only simple database queries.

SQLpage is an SQL-only webapp builder. It is meant for data scientists, analysts, and business intelligence teams to build powerful data-centric applications quickly, without worrying about any of the traditional web programming languages and concepts.

With SQLPage, you write simple .sql files containing queries to your database to select, group, update, insert, and delete your data, and you get good-looking clean webpages displaying your data as text, lists, grids, plots, and forms.

[SQLPage @ GitHub](https://github.com/lovasoa/SQLPage).]]>
            </summary>
            <updated>2025-08-29T06:45:44+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5353</id>
            <title type="text"><![CDATA[LAION-5B]]></title>
            <link rel="alternate" href="https://laion.ai/blog/laion-5b/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5353"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[A NEW ERA OF OPEN LARGE-SCALE MULTI-MODAL DATASETS | LAION.

We present a dataset of 5,85 billion CLIP-filtered image-text pairs, 14x bigger than LAION-400M, previously the biggest openly accessible image-text dataset in the world]]>
            </summary>
            <updated>2025-08-29T06:48:47+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5387</id>
            <title type="text"><![CDATA[Milvus]]></title>
            <link rel="alternate" href="https://milvus.io/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5387"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Milvus is an open-source vector database built to power embedding similarity search and AI applications. Milvus makes unstructured data search more accessible, and provides a consistent user experience regardless of the deployment environment.

- [Milvus @ GitHub](https://github.com/milvus-io/milvus).

Related contents:

- [RAG Against The Machine @ Quoi de neuf les devs ? :fr:](https://happytodev.substack.com/p/quoi-de-neuf-les-devs-153-veille?open=false#%C2%A7rag-against-the-machine).]]>
            </summary>
            <updated>2025-10-29T13:14:35+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5476</id>
            <title type="text"><![CDATA[Apache Superset]]></title>
            <link rel="alternate" href="https://superset.apache.org/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5476"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Superset is a modern data exploration and data visualization platform. Superset can replace or augment proprietary business intelligence tools for many teams. Superset integrates well with a variety of data sources.

- [Superset @ GitHub](https://github.com/apache/superset)]]>
            </summary>
            <updated>2025-09-19T12:23:16+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5481</id>
            <title type="text"><![CDATA[Visual Capitalist]]></title>
            <link rel="alternate" href="https://www.visualcapitalist.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5481"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[]]>
            </summary>
            <updated>2025-08-29T07:11:58+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5487</id>
            <title type="text"><![CDATA[Climate TRACE]]></title>
            <link rel="alternate" href="https://climatetrace.org/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5487"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Independent Greenhouse Gas emissions tracking.]]>
            </summary>
            <updated>2025-08-29T07:12:02+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5503</id>
            <title type="text"><![CDATA[Graphic Walker]]></title>
            <link rel="alternate" href="https://github.com/Kanaries/graphic-walker" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5503"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[An open source alternative to Tableau. Easily embedded as a component in web apps.

Graphic Walker is a different type of open-source alternative to Tableau. It allows data scientists to analyze data and visualize patterns with simple drag-and-drop operations.]]>
            </summary>
            <updated>2025-08-29T07:14:01+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5504</id>
            <title type="text"><![CDATA[PyGWalker]]></title>
            <link rel="alternate" href="https://github.com/Kanaries/pygwalker" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5504"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Turn your pandas dataframe into a Tableau-style User Interface for visual analysis.

PyGWalker: A Python Library for Exploratory Data Analysis with Visualization]]>
            </summary>
            <updated>2025-08-29T07:14:02+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5593</id>
            <title type="text"><![CDATA[Mobile Journalism Manual]]></title>
            <link rel="alternate" href="https://www.mojo-manual.org/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5593"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Storytelling, Data Journalism &amp;amp; Online Investigation - learn the basics of modern journalistic work with this new, immersive manual.]]>
            </summary>
            <updated>2025-08-29T07:29:08+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5602</id>
            <title type="text"><![CDATA[Mathesar]]></title>
            <link rel="alternate" href="https://mathesar.org/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5602"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Open source UI for Postgres databases.

Mathesar is a straightforward open source tool that provides a spreadsheet-like interface to a PostgreSQL database. Our web-based interface helps you and your collaborators work with data more independently and comfortably – no technical skills needed.

You can use Mathesar to build data models, enter data, and even build reports. You host your own Mathesar installation, which gives you ownership, privacy, and control of your data.

[Mathesar @ GitHub](https://github.com/centerofci/mathesar)]]>
            </summary>
            <updated>2025-08-29T07:32:08+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5608</id>
            <title type="text"><![CDATA[AIcyclopedia]]></title>
            <link rel="alternate" href="https://www.aicyclopedia.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5608"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[The free AI encyclopedia.
AI tools, podcasts, prompts, newsletter, and movies.]]>
            </summary>
            <updated>2025-08-29T07:32:13+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5631</id>
            <title type="text"><![CDATA[Papers With Code]]></title>
            <link rel="alternate" href="https://paperswithcode.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5631"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[The latest in Machine Learning]]>
            </summary>
            <updated>2025-08-29T07:36:14+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5644</id>
            <title type="text"><![CDATA[Kaggle API]]></title>
            <link rel="alternate" href="https://github.com/Kaggle/kaggle-api" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5644"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Official API for https://www.kaggle.com, accessible using a command line tool implemented in Python 3.]]>
            </summary>
            <updated>2025-08-29T07:38:11+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5721</id>
            <title type="text"><![CDATA[Kangas]]></title>
            <link rel="alternate" href="https://github.com/comet-ml/kangas" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5721"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[🦘 Explore multimedia datasets at scale.

Kangas is a tool for exploring, analyzing, and visualizing large-scale multimedia data. It provides a straightforward Python API for logging large tables of data, along with an intuitive visual interface for performing complex queries against your dataset.]]>
            </summary>
            <updated>2025-08-29T07:50:20+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5896</id>
            <title type="text"><![CDATA[Optuna]]></title>
            <link rel="alternate" href="https://optuna.org/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5896"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[A hyperparameter optimization framework. Optimize Your Optimization.

An open source hyperparameter optimization framework to automate hyperparameter search

[Optuna @ GitHub](https://github.com/optuna/optuna).]]>
            </summary>
            <updated>2025-08-29T08:20:36+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5907</id>
            <title type="text"><![CDATA[SpiffWorkflow]]></title>
            <link rel="alternate" href="https://www.spiffworkflow.org/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5907"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Executable Diagrams.
Connect people and software together in a way that makes sense to everyone. 

SpiffArena is a low(ish)-code software development platform for building, running, and monitoring executable diagrams. It is intended to support Citizen Developers and to enhance their ability to contribute to the software development process. Using tools that look a lot like flow-charts and spreadsheets, it is possible to capture complex rules in a way that everyone in your organization can see, understand, and directly execute.

[SpiffWorkflow @ GitHub](https://github.com/sartography/SpiffWorkflow)]]>
            </summary>
            <updated>2025-12-23T13:52:38+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/6047</id>
            <title type="text"><![CDATA[JupyterHub]]></title>
            <link rel="alternate" href="https://jupyter.org/hub" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/6047"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[With JupyterHub you can create a multi-user Hub that spawns, manages, and proxies multiple instances of the single-user Jupyter notebook server.
Project Jupyter created JupyterHub to support many users. The Hub can offer notebook servers to a class of students, a corporate data science workgroup, a scientific research project, or a high-performance computing group.
JupyterHub brings the power of notebooks to groups of users. It gives users access to computational environments and resources without burdening the users with installation and maintenance tasks. Users - including students, researchers, and data scientists - can get their work done in their own workspaces on shared resources which can be managed efficiently by system administrators.
[JupyterHub @ GitHub](https://github.com/jupyterhub/jupyterhub)]]>
            </summary>
            <updated>2025-08-29T08:44:53+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/6155</id>
            <title type="text"><![CDATA[csvkit]]></title>
            <link rel="alternate" href="https://csvkit.readthedocs.io/en/latest/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/6155"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[csvkit is a suite of command-line tools for converting to and working with CSV, the king of tabular file formats.

- [csvkit @ GitHub](https://github.com/wireservice/csvkit)]]>
            </summary>
            <updated>2025-11-04T07:24:38+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/6168</id>
            <title type="text"><![CDATA[WebPlotDigitizer]]></title>
            <link rel="alternate" href="https://automeris.io/WebPlotDigitizer/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/6168"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Extract data from plots, images, and maps.
A web based tool to extract numerical data from plot images. Supports XY, Polar, Ternary diagrams and Maps. 
It is often necessary to reverse engineer images of data visualizations to extract the underlying numerical data. WebPlotDigitizer is a semi-automated tool that makes this process extremely easy.

[WebPlotDigitizer @ GitHub](https://github.com/ankitrohatgi/WebPlotDigitizer)]]>
            </summary>
            <updated>2025-08-29T09:05:01+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/6172</id>
            <title type="text"><![CDATA[dbt Learn]]></title>
            <link rel="alternate" href="https://courses.getdbt.com/collections" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/6172"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Level up your dbt™ skills on your own time with dbt Fundamentals.Learn Analytics Engineering with dbt.]]>
            </summary>
            <updated>2025-08-29T09:05:58+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/6179</id>
            <title type="text"><![CDATA[YOLOv5]]></title>
            <link rel="alternate" href="https://github.com/ultralytics/yolov5" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/6179"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[YOLOv5 in PyTorch &amp;gt; ONNX &amp;gt; CoreML &amp;gt; TFLite.
YOLOv5 is the world&amp;#039;s most loved vision AI, representing Ultralytics open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development.]]>
            </summary>
            <updated>2025-08-29T09:06:58+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/6194</id>
            <title type="text"><![CDATA[Python Graph Gallery]]></title>
            <link rel="alternate" href="https://www.python-graph-gallery.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/6194"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Welcome to the Python Graph Gallery, a collection of hundreds of charts made with Python. Charts are organized in about 40 sections and always come with their associated reproducible code. They are mostly made with Matplotlib and Seaborn but other library like Plotly are sometimes used.]]>
            </summary>
            <updated>2025-08-29T09:09:05+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/6215</id>
            <title type="text"><![CDATA[PRQL]]></title>
            <link rel="alternate" href="https://prql-lang.org/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/6215"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Pipelined Relational Query Language, pronounced &amp;quot;Prequel&amp;quot;.
PRQL is a modern language for transforming data — a simple, powerful, pipelined SQL replacement. Like SQL, it&amp;#039;s readable, explicit and declarative. Unlike SQL, it forms a logical pipeline of transformations, and supports abstractions such as variables and functions. It can be used with any database that uses SQL, since it compiles to SQL.

[PRQL @ GitHub](https://github.com/PRQL/prql)]]>
            </summary>
            <updated>2025-08-29T09:13:07+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/6223</id>
            <title type="text"><![CDATA[Streamlit]]></title>
            <link rel="alternate" href="https://streamlit.io/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/6223"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[A faster way to build and share data apps.
Streamlit turns data scripts into shareable web apps in minutes.
All in pure Python. No front‑end experience required.

- [Streamlit @ GitHub](https://github.com/streamlit/streamlit).]]>
            </summary>
            <updated>2025-08-29T09:14:03+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/6346</id>
            <title type="text"><![CDATA[Data FAIR]]></title>
            <link rel="alternate" href="https://github.com/data-fair/data-fair" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/6346"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Findable, Accessible, Interoperable and Reusable Data. A complete open-source solution for your open and private data needs. French only for the time being, internationalization coming soon.]]>
            </summary>
            <updated>2025-08-29T09:35:12+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/6530</id>
            <title type="text"><![CDATA[Katana]]></title>
            <link rel="alternate" href="https://github.com/projectdiscovery/katana" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/6530"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[A next-generation crawling and spidering framework]]>
            </summary>
            <updated>2025-08-29T10:05:34+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/6757</id>
            <title type="text"><![CDATA[Tremor]]></title>
            <link rel="alternate" href="https://github.com/tremorlabs/tremor" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/6757"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[The react library to build dashboards fast.
Tremor lets you create simple and modular components to build insightful dashboards in a breeze. Fully open-source, made by data scientists and software engineers with a sweet spot for design.]]>
            </summary>
            <updated>2025-08-29T10:43:50+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/7089</id>
            <title type="text"><![CDATA[Data science ipython notebooks master]]></title>
            <link rel="alternate" href="https://pythonawesome.com/data-science-ipython-notebooks-master/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/7089"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Tutorials notebooks for data science]]>
            </summary>
            <updated>2025-08-29T11:38:34+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/7240</id>
            <title type="text"><![CDATA[Metabase]]></title>
            <link rel="alternate" href="https://www.metabase.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/7240"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Open Source Business Intelligence

 The simplest, fastest way to get business intelligence and analytics to everyone in your company 😋 

[Metabase @ GitHub](https://github.com/metabase/metabase).]]>
            </summary>
            <updated>2025-08-29T12:03:32+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/7247</id>
            <title type="text"><![CDATA[Kaggle]]></title>
            <link rel="alternate" href="https://www.kaggle.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/7247"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Your Machine Learning and Data Science Community.

Inside Kaggle you’ll find all the code &amp;amp; data you need to do your data science work. Use over 50,000 public datasets and 400,000 public notebooks to conquer any analysis in no time.]]>
            </summary>
            <updated>2025-08-29T12:06:32+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/7559</id>
            <title type="text"><![CDATA[Project Jupyter]]></title>
            <link rel="alternate" href="http://jupyter.org/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/7559"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[The Jupyter Notebook is a web application that allows you to create and share documents that contain live code, equations, visualizations and explanatory text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, machine learning and much more.]]>
            </summary>
            <updated>2025-08-29T12:57:03+00:00</updated>
        </entry>
    </feed>
