Analytics Hub

EDB delivers Analytics capabilities for Postgres, enabling you to run analytical queries over large datasets and more. And EDB Analytics lets you do it all in the Postgres ecosystem wherever you need it.

Concepts

• Why?: The need for analytics in Postgres arises from the growing demand for data-driven decision-making and the need to analyze large datasets efficiently.
• Analytics Engine: A vectorized SQL query engine that executes analytical queries over columnar data in object storage, built on Apache DataFusion and optimized for performance.
• Lakehouse: A data architecture that combines the best of data lakes and data warehouses, allowing you to store and analyze data in a single platform.
• Storage locations: The physical or logical locations where data is stored, such as S3 buckets or on-premises storage systems.
• Catalog: A metadata repository that stores information about the data stored in a Lakehouse, including table definitions, schemas, and data locations.
• Tiered Tables: A feature of EDB Postgres Distributed (PGD) that allows you to store data in different storage locations based on its usage patterns, optimizing performance and cost.

Use cases

• Read-only analytics without a Catalog
  • "I need to run analytical queries over S3 delta tables and I want to use Postgres"
  • "I need to run analytical queries over Iceberg data and I want to use Postgres"
• Read-write analytics without a Catalog
  • "I need to offload data to S3 while keeping it available for analytics"
  • "I need to offload tables data to S3 while keeping it available for queries and analytics"
• Read-write analytics with a Catalog
  • "I want to read and write data to a Catalog"
• Lakehouse read-only analytics
  • "I need a Managed Lakehouse to read and analyze data stored as Delta Tables, Iceberg or a Catalog"