TigerData logo

The #1 Time-Series Database

Keep sensor, on-chain, and customer data fresh, with compressed history and queryable real-time analytics at production scale.

Start a free trialContact sales

100% PostgreSQL compatible

Open source

23.0k github stars

// Why TimescaleDB

Core Capabilities

Gold Partner with Inductive Automation — Ignition

Automatic Partitioning

Hypertables turn any Postgres table into a table automatically partitioned by time or id for fast ingest and predictable queries.
  • Partition skipping at query planning
  • Efficient index-only & skip-scans

Row/columnar Hybrid Storage

Keep years of history online at a fraction of the cost, while making your analytical queries even faster.
  • Auto-conversion between row/columnstore
  • Vectorized operators with SIMD

Compression (up to 95%)

Keep years of history online at a fraction of the cost, while making your analytical queries even faster.
  • Delta, dictionary, and RLE encodings
  • Direct filtering on compressed data

Incremental Materialized Views

Our continuous aggregates (caggs) enable incrementally refreshed rollups for instant dashboards.
  • Parallelized batched refreshes
  • Real-time mode includes latest changes

Automated Management

First-class automation for columnstore, retention, and aggregate refresh with full auditability.
  • Built-in job scheduler with retries
  • Configurable retention policies

Time-series Functions

Hyperfunctions simplify fast, advanced time-series analysis using ~200 native SQL functions.
  • Time-weighted averages & interpolation
  • Partial aggregations eliminate reprocessing

What makes Tiger Data so fast?

Filter fast. Scan less. Execute in parallel.

Tiger Data filters out irrelevant data before the query even begins—eliminating time-based partitions and using metadata to skip over large blocks of data.

The rowstore handles high-ingest and point queries with speed, while the columnstore powers fast, parallel scans over compressed columnar data.

With vectorized execution and SIMD acceleration, even complex queries return in an instant.

hypertable gif
Learn more about the Architecture

// case studies

Customer Results

35x performance improvements

For many teams at Cloudflare, TimescaleDB strikes a phenomenal balance between the simplicity of storing your analytical data under the same roof as your configuration data, while also gaining much of the impressive performance of a specialized OLAP system.

Robert CepaSr Software Engineer, Cloudflare
Learn more

With Tiger Data's proven scalability and performance at its core, Replicated ensures that even as SecureBuild continues to grow, enterprises and developers alike will benefit from fast, dependable insights and hardened software delivery.

Grant MillerCEO
Learn more

500M+ DAILY VOLUME

Other vector databases and logging platforms were either too rigid, too expensive, or didn't allow us to keep things Postgres-native.

FOUNDER
Learn more
Read customer stories

Open source at our core.
Powered by community.

TimescaleDB is built in the open. Join thousands of developers, engineers, and data scientists pushing the boundaries of what's possible with PostgreSQL.

22.0K+

GitHub Stars

Contribute to the core engine, open issues, and shape the roadmap.

Star on GitHub
12,000+

Slack Members

Get help, share your architecture, and talk time-series with experts.

Join our Slack
$ helm repo add timescale https://charts.timescale.com # "timescale" has been added to your repositories $ helm install my-release timescale/timescaledb # NAME: my-release # LAST DEPLOYED: Tue Mar 03 11:31:23 2026 # NAMESPACE: default # STATUS: deployed # Welcome to the TimescaleDB community! # You are now ready to scale PostgreSQL. $ █

Ready to query billions of rows in milliseconds?

Start analyzing time-series and events with the performance of a specialized database and the familiarity of PostgreSQL.

Start a free trialDownload open-source