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What is TimescaleDB?

TimescaleDB is an advanced time-series and analytics database built entirely on top of PostgreSQL, combining the best of relational reliability and time-series speed. It’s engineered to help developers and data teams analyze streaming, sensor, and event data in real time, while retaining historical data cost-effectively. Its core innovation, the hypertable, automatically partitions large datasets across time and space, optimizing query planning and ingestion for billions of records. TimescaleDB’s continuous aggregates provide incrementally refreshed views, enabling instant dashboards and analytics without costly recomputations. It also offers hybrid row-columnar storage, blending transactional speed with analytical performance, and supports compression rates up to 95% for long-term data storage. With built-in automation for retention, aggregation, and reordering, it reduces the operational overhead of managing time-series data at scale. TimescaleDB’s hyperfunctions library extends SQL with over 200 specialized time-series analysis functions — ideal for anomaly detection, forecasting, and performance tracking. Because it’s 100% PostgreSQL compatible, teams can leverage existing Postgres tools, drivers, and extensions while gaining time-series capabilities instantly. Open-source and cloud-ready, it powers critical workloads for industries ranging from IoT and fintech to cloud infrastructure monitoring. With TimescaleDB, developers can query billions of data points in milliseconds — using the same SQL they already know.

What is Citus?

Citus enriches the widely appreciated Postgres experience by offering distributed table capabilities while being entirely open source. It now accommodates both schema-based and row-based sharding, ensuring compatibility with Postgres 16. You can effectively scale Postgres by distributing data and queries, starting with a single Citus node and smoothly incorporating additional nodes and rebalancing shards as your requirements grow. By leveraging parallelism, keeping a larger dataset in memory, boosting I/O bandwidth, and using columnar compression, query performance can be significantly enhanced, achieving speeds up to 300 times or even more. As an extension rather than a separate fork, Citus remains compatible with the latest Postgres versions, allowing you to leverage your existing SQL expertise and tools. Furthermore, it enables you to address infrastructure challenges by managing both transactional and analytical workloads within one database system. Available for free as open source, Citus allows for self-management while also inviting contributions to its development via GitHub. Transitioning your focus from database management to application development becomes easier as you run your applications on Citus within the Azure Cosmos DB for PostgreSQL environment, thus streamlining your workflow. This integration not only boosts efficiency but also empowers developers to harness the full potential of scalable, high-performance database solutions.

Media

Media

Integrations Supported

PostgreSQL
DataClarity Unlimited Analytics
Dataplane
Last9
MyDBA.dev
Ndustrial Contxt
NexClipper
TrendMiner
Workers by Delos
Wyn Enterprise
n8n

Integrations Supported

PostgreSQL
Algolia
Azure Cosmos DB
Debian
Fedora
Freshworks Neo
GitHub
Microsoft Azure
MixRank
PEX

API Availability

API Availability

Pricing Information

Pricing not provided
Free Version
Free Trial Offered?

Pricing Information

$0.27 per hour
Free Version

Supported Platforms

SaaS
On-Prem

Supported Platforms

SaaS
On-Prem

Customer Service / Support

Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars

Training Options

Documentation Hub
Webinars
On-Site Training

Company Facts

Organization Name

Tiger Data

Date Founded

2015

Company Location

United States

Company Website

www.tigerdata.com/timescaledb

Company Facts

Organization Name

Citus Data

Company Location

United States

Company Website

www.citusdata.com

Categories and Features

Relational Database

Not specified

Time Series Databases

Not specified

Categories and Features

Distributed Databases

Not specified

OLAP Databases

Not specified

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