Get to know Tiger Data

Explore by product

Tiger Data products are built on TimescaleDB, the open-source PostgreSQL extension for time-series and real-time analytics. You can run it yourself, or choose a product that packages the same engine with production operations and support.

Tiger Cloud

Fully managed cloud database service on AWS and Azure

TimescaleDB Enterprise

Self-managed and commercially licensed, for on-premises, edge, and air-gapped environments

Explore by topic

These cover the TimescaleDB engine and data-management concepts, which work the same across self-hosted TimescaleDB, Tiger Cloud, TimescaleDB Enterprise, and Managed Service for TimescaleDB. Some features noted below, like tiered storage, are specific to a particular product.

Overview & architecture

Architectural choices and optimizations that power TimescaleDB and Tiger Cloud for real-time analytics

Capabilities & comparison

How TimescaleDB capabilities fit together and how Tiger Cloud and self-hosted options compare

Table layout: wide, narrow, or medium

How table shape affects metrics, schema changes, and queries before you commit to a hypertable

Data model: keys and time columns

Partition columns, primary keys, and unique constraints for hypertables, before you load data

Hypertables & chunks

Time partitioning, chunk intervals, and how data is organized at scale

Hypercore

Columnar storage, compression, and how hypercore relates to hypertables and chunks

Continuous aggregates (CAGGs)

How continuous aggregates work, real-time aggregates and hierarchical aggregates, materialized hypertables, and timezone handling

Data lifecycle

Retention, tiered storage, and Tiger Cloud policies for dropping old data and moving colder data

Deep dive

Hub for advanced topics and longer-form architecture material beyond the whitepaper

Glossary

Definitions for engineering, database, and product terms used throughout the docs

For quickstarts, examples, and how-tos, see Build.