Analytics/Lakehouse

Use the Analytics Accelerator to explore the analytical capabilities built on EDB Postgres®. This accelerator helps you understand core concepts, explore key technologies such as EDB Postgres® Lakehouse, and learn how to implement analytics with EDB Hybrid Manager (HM).

We integrate modern data architectures and open standards with the reliability and flexibility of Postgres to help you unlock valuable insights.

The accelerator organizes content into four areas:

  • Conceptual foundations Build your understanding of analytics principles and EDB’s approach.

  • EDB core analytics technologies Learn about EDB solutions and technologies that power our analytics offerings.

  • Practical guidance and solutions Find use cases, persona-based guides, how-to articles, and tutorials.

  • Product-specific implementations Access documentation for how these analytics capabilities surface and are managed in EDB products, such as EDB Hybrid Manager.

Conceptual foundations

Understand the principles and strategies behind modern data analytics and EDB’s approach.

  • Generic analytics concepts Learn about data architectures (Data Warehouse, Data Lake, Lakehouse) and foundational technologies (columnar storage, vectorized engines, and others).

  • EDB analytics concepts Explore EDB’s vision for Postgres® analytics and how EDB leverages core technologies.

  • Explained: Analytics Review in-depth explanations of EDB analytical features, design choices, and advanced topics. (Coming soon)

EDB core analytics technologies

Learn about EDB’s analytics technologies and how they extend Postgres®.

Practical guidance and solutions

Apply EDB’s analytics capabilities to meet your needs.

Product-specific implementations

Review how EDB analytics concepts and technologies are implemented in EDB products.

Where to start

Postgres Lakehouse is built using a number of technologies:

Level 100

The most important thing to understand about Postgres Lakehouse is that it separates storage from compute. This design allows you to scale them independently, which is ideal for analytical workloads where queries can be unpredictable and spiky. You wouldn't want to keep a machine mostly idle just to hold data on its attached hard drives. Instead, you can keep data in object storage (and also in highly compressible formats), and only provision the compute needed to query it when necessary.

Level 100 Architecture

On the compute side, a vectorized query engine is optimized to query Lakehouse tables but still fall back to Postgres for full compatibility.

On the storage side, Lakehouse tables are stored using highly compressible columnar storage formats optimized for analytics.

Level 200

Here's a slightly more comprehensive diagram of how these services fit together:

Level 200 Architecture

Level 300

Here's the more detailed, zoomed-in view of "what's in the box":

Level 300 Architecture

Quick start

Launch a Lakehouse node and query sample data.

Tiered Tables

Understand how Tiered Tables enable cost-efficient analytics and data management with EDB Postgres Distributed (PGD) and the Analytics Accelerator.

Reference

Things to know about EDB Postgres® AI Lakehouse

Delta Lake

Understand how Delta Lake enhances modern data lakes and how Analytics Accelerator leverages it within the EDB Postgres ecosystem.

Iceberg

Understand how Apache Iceberg enables scalable, reliable data lake storage and how Analytics Accelerator leverages it within the EDB Postgres ecosystem.

Lakehouse

Understand how the EDB Postgres Lakehouse architecture supports fast, scalable analytics on modern data lake storage.

Learning Resources

Navigate Analytics Accelerator documentation with explanations, tutorials, how-to guides, use cases, persona-based guidance, and structured learning paths.

WarehousePG and EDB Postgres AI support for Greenplum workloads

Covering the open source WarehousePG and EDB Postgres AI support for Greenplum workloads with WarehousePG.


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