<?xml version="1.0" encoding="UTF-8"?>
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    <title>time-series</title>
    <link rel="self" type="application/atom+xml" href="https://links.biapy.com/guest/tags/737/feed"/>
    <updated>2026-04-21T18:11:21+00:00</updated>
    <id>https://links.biapy.com/guest/tags/737/feed</id>
            <entry>
            <id>https://links.biapy.com/links/12275</id>
            <title type="text"><![CDATA[OpenData]]></title>
            <link rel="alternate" href="https://www.opendata.dev/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/12275"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Object-store native databases built on a common foundation. Simple to operate. Impossible to outgrow. 

OpenData is a collection of open source databases built on a common, object-native storage and infrastructure foundation. This shared foundation means every database has a virtually identical operational profile, which makes our database fleet materially easier and cheaper to operate than alternatives.

- [OpenData @ GitHub](https://github.com/opendata-oss/opendata).

Related contents:

- [the broken economics of databases @ bits&amp;amp;pages](https://www.bitsxpages.com/p/the-broken-economics-of-databases).]]>
            </summary>
            <updated>2026-03-24T13:28:17+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/11163</id>
            <title type="text"><![CDATA[TDengine]]></title>
            <link rel="alternate" href="https://tdengine.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/11163"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[AI-Driven Data Platform for the Industrial IoT.
High-performance, scalable time-series database designed for Industrial IoT (IIoT) scenarios.

TDengine is an open source, high-performance, cloud native and AI powered time-series database designed for Internet of Things (IoT), Connected Cars, and Industrial IoT. It enables efficient, real-time data ingestion, processing, and analysis of TB and even PB scale data per day, generated by billions of sensors and data collectors.

- [TDengine @ GitHub](https://github.com/taosdata/TDengine).]]>
            </summary>
            <updated>2025-12-05T10:10:29+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/11104</id>
            <title type="text"><![CDATA[Apache TsFile]]></title>
            <link rel="alternate" href="https://tsfile.apache.org/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/11104"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[File Format for Internet of Things

TsFile is a columnar storage file format designed for time series data, which supports efficient compression, high throughput of read and write, and compatibility with various frameworks, such as Spark and Flink. It is easy to integrate TsFile into IoT big data processing frameworks.

- [Apache TsFile @ GitHub](https://github.com/apache/tsfile).]]>
            </summary>
            <updated>2025-11-26T12:41:09+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/10722</id>
            <title type="text"><![CDATA[Arc]]></title>
            <link rel="alternate" href="https://basekick.net/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/10722"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Fastest Time-Series Database.
 High-performance time-series data warehouse built on DuckDB and Parquet with flexible storage options. 

Time-series data warehouse built for speed. 2.42M records/sec on local NVMe. DuckDB + Parquet + Arrow + flexible storage (local/MinIO/S3). AGPL-3.0 

- [Arc @ GitHub](https://github.com/Basekick-Labs/arc).]]>
            </summary>
            <updated>2025-10-20T06:41:58+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/10337</id>
            <title type="text"><![CDATA[Prophet]]></title>
            <link rel="alternate" href="https://facebook.github.io/prophet/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/10337"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Forecasting at scale.

 Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth. 

Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well.

- [Prophet: Automatic Forecasting Procedure @ GitHub](https://github.com/facebook/prophet).

Related contents:

- [Predictive Autoscaling in Kubernetes with Keda and Prophet @ Minimal Devops&amp;#039; Medium](https://minimaldevops.com/predictive-autoscaling-in-kubernetes-with-keda-and-prophet-cbccd96cf881).]]>
            </summary>
            <updated>2025-09-22T07:10:10+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/10111</id>
            <title type="text"><![CDATA[TimesFM (Time Series Foundation Model)]]></title>
            <link rel="alternate" href="https://github.com/google-research/timesfm" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/10111"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting. 

Related contents:

- [Google sort TimesFM, son modèle IA qui prédit l&amp;#039;avenir des séries temporelles @ Korben :fr:](https://korben.info/google-timesfm-modele-prevision.html).]]>
            </summary>
            <updated>2025-09-11T06:20:08+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/650</id>
            <title type="text"><![CDATA[GreptimeDB]]></title>
            <link rel="alternate" href="https://greptime.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/650"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[The Single Database for Big Observability. Fast, Efficient, Single Database for Real-Time Observability.
The real-time, cloud-native observability database for metrics, logs, and traces, providing sub-second insights from edge to cloud—at any scale.

- [GreptimeDB @ GitHub](https://github.com/GreptimeTeam/greptimedb)

Related contents:

- [GreptimeDB as Prometheus Long-term Storage @ Anarcher&amp;#039;s Trashcan](https://blog.anarcher.dev/post/2025/04-13-greptimedb-intro/).]]>
            </summary>
            <updated>2025-08-28T17:46:17+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/695</id>
            <title type="text"><![CDATA[QuestDB]]></title>
            <link rel="alternate" href="https://questdb.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/695"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Next-generation time-series database. Peak time-series performance.

QuestDB is the world&amp;#039;s fastest growing open-source time-series database. It offers massive ingestion throughput, millisecond queries, powerful time-series SQL extensions, and scales well with minimal and maximal hardware. Save costs with better performance and efficiency.

- [QuestDB @ GitHub](https://github.com/questdb/questdb).]]>
            </summary>
            <updated>2025-08-28T17:54:15+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1068</id>
            <title type="text"><![CDATA[VictoriaLogs]]></title>
            <link rel="alternate" href="https://victoriametrics.com/products/victorialogs/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1068"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Scalable, Open Source, Logs DB &amp;amp; Logging Solution.

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

Related contents:

- [Grepping logs remains terrible @ Chronicae Novis Rebus](https://chronicles.mad-scientist.club/tales/grepping-logs-remains-terrible/).]]>
            </summary>
            <updated>2025-08-28T18:54:59+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2072</id>
            <title type="text"><![CDATA[M3]]></title>
            <link rel="alternate" href="https://m3db.io/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2072"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Open Source Metrics Engine.
Distributed TSDB and Query Engine, Prometheus Sidecar, Metrics Aggregator, and more such as Graphite storage and query engine.

M3 is a Prometheus compatible, easy to adopt metrics engine that provides visibility for some of the world’s largest brands. 

- [M3 @ GitHub](https://github.com/m3db/m3).]]>
            </summary>
            <updated>2025-08-28T21:41:20+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3574</id>
            <title type="text"><![CDATA[Apache IoTDB]]></title>
            <link rel="alternate" href="https://iotdb.apache.org/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3574"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Apache IoTDB (Database for Internet of Things) is an IoT native database with high performance for data management and analysis, deployable on the edge and the cloud. Due to its light-weight architecture, high performance and rich feature set together with its deep integration with Apache Hadoop, Spark and Flink, Apache IoTDB can meet the requirements of massive data storage, high-speed data ingestion and complex data analysis in the IoT industrial fields. 

- [Apache IoTDB @ GitHub](https://github.com/apache/iotdb).]]>
            </summary>
            <updated>2025-08-29T01:53:14+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4021</id>
            <title type="text"><![CDATA[Timescale]]></title>
            <link rel="alternate" href="https://www.tigerdata.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4021"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[PostgreSQL ++ for time series and events.
An open-source time-series SQL database optimized for fast ingest and complex queries. Packaged as a PostgreSQL extension. 

TimescaleDB is an open-source database designed to make SQL scalable for time-series data. It is engineered up from PostgreSQL and packaged as a PostgreSQL extension, providing automatic partitioning across time and space (partitioning key), as well as full SQL support.

- [TimescaleDB @ GitHub](https://github.com/timescale/timescaledb).

Related contents:

- [Just Use Postgres for Everything @ Amazing CTO](https://www.amazingcto.com/postgres-for-everything/).
- [How TimescaleDB helped us scale analytics and reporting @ The Cloudflare Blog](https://blog.cloudflare.com/timescaledb-art/).]]>
            </summary>
            <updated>2025-08-29T03:06:54+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4374</id>
            <title type="text"><![CDATA[Warp 10]]></title>
            <link rel="alternate" href="https://warp10.io/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4374"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[The Most Advanced Time Series Platform.

Warp 10 is a modular open source platform shaped for the IoT that collects, stores and allows you to analyze sensor data. It offers both a Time Series Database and a powerful analysis environment that can be used together or independently.

- [Warp 10 @ GitHub](https://github.com/senx/warp10-platform).
- [Métriques : archiver des milliards de points chaque mois @ clever cloud :fr:](https://www.clever-cloud.com/fr/blog/engineering-fr/2024/04/04/metriques-archiver-milliards-de-points-tous-les-mois/).]]>
            </summary>
            <updated>2025-08-29T04:06:25+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5297</id>
            <title type="text"><![CDATA[QuestDB]]></title>
            <link rel="alternate" href="https://questdb.io/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5297"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Fast SQL for time-series



QuestDB is an open-source time-series database for high throughput ingestion and fast SQL queries with operational simplicity. It supports schema-agnostic ingestion using the InfluxDB line protocol, PostgreSQL wire protocol, and a REST API for bulk imports and exports.

[QuestDB @ GitHub](https://github.com/questdb/questdb).]]>
            </summary>
            <updated>2025-08-29T06:39:47+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5613</id>
            <title type="text"><![CDATA[Graphite]]></title>
            <link rel="alternate" href="https://graphiteapp.org/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5613"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Graphite make it easy to store and graph metrics.

Graphite is an enterprise-ready monitoring tool that runs equally well on cheap hardware or Cloud infrastructure. Teams use Graphite to track the performance of their websites, applications, business services, and networked servers. It marked the start of a new generation of monitoring tools, making it easier than ever to store, retrieve, share, and visualize time-series data.

[Graphite @ GitHub](https://github.com/graphite-project/)]]>
            </summary>
            <updated>2025-08-29T07:32:16+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/7159</id>
            <title type="text"><![CDATA[Warp 10 - The Most Advanced Time Series Platform]]></title>
            <link rel="alternate" href="https://www.warp10.io/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/7159"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Warp 10 is a modular open source platform shaped for the IoT that collects, stores and allows you to analyze sensor data.]]>
            </summary>
            <updated>2025-08-29T11:50:28+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/7587</id>
            <title type="text"><![CDATA[Prometheus]]></title>
            <link rel="alternate" href="https://prometheus.io/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/7587"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Monitoring system &amp;amp;amp; time series database.
Power your metrics and alerting with a leading open-source monitoring solution.

Related contents:

- [Scaling With Prometheus: Managing 80M Metrics Smoothly @ Kapil&amp;#039;s Medium](https://kapillamba4.medium.com/hierarchical-federation-in-prometheus-managing-millions-of-metrics-cleanly-8d8bac940ff3).
- [Monitoring a Streamlit App on Google GKE with Prometheus, Grafana, Loki &amp;amp; Alloy @ Omar Din&amp;#039;s Medium](https://medium.com/@omarnour_5895/monitoring-a-streamlit-app-on-gke-with-grafana-loki-alloy-4d1bad572c01).
- [Prometheus with Docker Compose: Guide &amp;amp; Examples @ spacelift](https://spacelift.io/blog/prometheus-docker-compose).
- [From Custom to Open: Scalable Network Probing and HTTP/3 Readiness with Prometheus @ Slack Engineering](https://slack.engineering/from-custom-to-open-scalable-network-probing-and-http-3-readiness-with-prometheus/).]]>
            </summary>
            <updated>2026-04-13T04:10:29+00:00</updated>
        </entry>
    </feed>
