<?xml version="1.0" encoding="UTF-8"?>
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    <title>olap</title>
    <link rel="self" type="application/atom+xml" href="https://links.biapy.com/guest/tags/1537/feed"/>
    <updated>2026-04-24T08:57:16+00:00</updated>
    <id>https://links.biapy.com/guest/tags/1537/feed</id>
            <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/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/4053</id>
            <title type="text"><![CDATA[YDB]]></title>
            <link rel="alternate" href="https://ydb.tech/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4053"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[an open source Distributed SQL Database.

YDB is a versatile open source Distributed SQL Database that combines high availability and scalability with strong consistency and ACID transactions. It accommodates transactional (OLTP), analytical (OLAP), and streaming workloads simultaneously.

- [YDB @ GitHub](https://github.com/ydb-platform/ydb).]]>
            </summary>
            <updated>2025-08-29T03:11:57+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/6382</id>
            <title type="text"><![CDATA[DuckDB]]></title>
            <link rel="alternate" href="https://duckdb.org/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/6382"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[DuckDB is an in-process SQL OLAP database management system.

DuckDB is a high-performance analytical database system. It is designed to be fast, reliable, portable, and easy to use. DuckDB provides a rich SQL dialect, with support far beyond basic SQL
DuckDB supports arbitrary and nested correlated subqueries, window functions, collations, complex types (arrays, structs, maps), and several extensions designed to make SQL easier to use.

- [DuckDB @ GitHub](https://github.com/duckdb/duckdb).

Related contents:

- [DuckDB - Le moteur SQL qui transforme vos données @ Korben :fr:](https://korben.info/duckdb-moteur-sql-transformation-donnees.html).
- [Why DuckDB is my first choice for data processing @ \&amp;gt;robinlinacre](https://www.robinlinacre.com/recommend_duckdb/#why-duckdb-is-my-first-choice-for-data-processing).
- [DuckDB is Probably the Most Important Geospatial Software of the Last Decade @ dbreunig.com](https://www.dbreunig.com/2025/05/03/duckdb-is-the-most-impactful-geospatial-software-in-a-decade.html).
- [Why Semantic Layers Matter — and How to Build One with DuckDB @ MotherDuck](https://motherduck.com/blog/semantic-layer-duckdb-tutorial/).
- [Querying Billions of GitHub Events Using Modal and DuckDB (Part 1: Ingesting Data) @ noreasontopanic](https://noreasontopanic.com/p/querying-billions-of-github-events).
- [DuckDB beats Polars for 1TB of data @ Confessions of a Data Guy](https://www.confessionsofadataguy.com/duckdb-beats-polars-for-1tb-of-data/).
- [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).
- [Building an Obsidian RAG with DuckDB and MotherDuck @ MotherDuck](https://motherduck.com/blog/obsidian-rag-duckdb-motherduck/).]]>
            </summary>
            <updated>2026-02-16T06:10:15+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/7492</id>
            <title type="text"><![CDATA[Sybil]]></title>
            <link rel="alternate" href="https://github.com/logv/sybil" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/7492"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[a fast and simple NoSQL OLAP.
Sybil is an append only analytics datastore with no up front table schema requirements; just log JSON records to a table and run queries. Written in Go, sybil is designed for fast full table scans of multi-dimensional data on a single machine.]]>
            </summary>
            <updated>2025-08-29T12:46:59+00:00</updated>
        </entry>
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