---
title: Get to know Tiger Data | Tiger Data Docs
description: Concepts, comparisons, architecture, how features connect, and the glossary
---

## 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](/learn/tiger-cloud/index.md)

[Fully managed cloud database service on AWS and Azure](/learn/tiger-cloud/index.md)

[TimescaleDB Enterprise](/learn/enterprise/index.md)

[Self-managed and commercially licensed, for on-premises, edge, and air-gapped environments](/learn/enterprise/index.md)

## 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](/learn/deep-dive/whitepaper/index.md)

[Architectural choices and optimizations that power TimescaleDB and Tiger Cloud for real-time analytics](/learn/deep-dive/whitepaper/index.md)

[Capabilities & comparison](/learn/capabilities-and-comparison/understand-capabilities/index.md)

[How TimescaleDB capabilities fit together and how Tiger Cloud and self-hosted options compare](/learn/capabilities-and-comparison/understand-capabilities/index.md)

[Table layout: wide, narrow, or medium](/learn/data-model/wide-narrow-medium-tables/index.md)

[How table shape affects metrics, schema changes, and queries before you commit to a hypertable](/learn/data-model/wide-narrow-medium-tables/index.md)

[Data model: keys and time columns](/learn/data-model/primary-keys-time-and-uniqueness/index.md)

[Partition columns, primary keys, and unique constraints for hypertables, before you load data](/learn/data-model/primary-keys-time-and-uniqueness/index.md)

[Hypertables & chunks](/learn/hypertables/understand-hypertables/index.md)

[Time partitioning, chunk intervals, and how data is organized at scale](/learn/hypertables/understand-hypertables/index.md)

[Hypercore](/learn/columnar-storage/understand-hypercore/index.md)

[Columnar storage, compression, and how hypercore relates to hypertables and chunks](/learn/columnar-storage/understand-hypercore/index.md)

[Continuous aggregates (CAGGs)](/learn/continuous-aggregates/index.md)

[How continuous aggregates work, real-time aggregates and hierarchical aggregates, materialized hypertables, and timezone handling](/learn/continuous-aggregates/index.md)

[Data lifecycle](/learn/data-lifecycle/data-retention/about-data-retention/index.md)

[Retention, tiered storage, and Tiger Cloud policies for dropping old data and moving colder data](/learn/data-lifecycle/data-retention/about-data-retention/index.md)

[Deep dive](/learn/deep-dive/index.md)

[Hub for advanced topics and longer-form architecture material beyond the whitepaper](/learn/deep-dive/index.md)

[Glossary](/learn/glossary/index.md)

[Definitions for engineering, database, and product terms used throughout the docs](/learn/glossary/index.md)

For quickstarts, examples, and how-tos, see [Build](/build/index.md).