Analytics Accelerator concepts and technologies

Analytics Accelerator concepts and technologies#

Use this section to build a clear understanding of the core concepts, technologies, architectural patterns, and strategies that define the Analytics Accelerator.

This section complements:

For explanations specific to the EDB Hybrid Manager (HM) environment, see Analytics in Hybrid Manager .

Analytics Accelerator concepts and technologies#

Foundational analytics and modern data architectures#

Understand the industry trends and architectural patterns that shape today’s analytics landscape.

Generic concepts#

Foundational terms such as data warehouses, data lakes, lakehouse architecture, columnar storage, vectorized engines, and separation of storage and compute.

Additional articles (coming soon)#

  • The evolution to the data lakehouse

  • The significance of open table formats (Iceberg, Delta Lake, Hudi)

  • Benefits and trade-offs of columnar vs. row-oriented storage for analytics

Analytics Accelerator concepts and technologies#

Deep dive into core Analytics Accelerator components#

Learn about the core components that enable advanced analytics on EDB Postgres.

EDB Postgres Lakehouse#

  • EDB Postgres Lakehouse: An overview (coming soon)

  • EDB Postgres Lakehouse: Detailed concepts and terminology (coming soon)

Open table formats with EDB Postgres#

  • Understanding Apache Iceberg with EDB solutions (coming soon)

  • Understanding Delta Lake with EDB solutions (coming soon)

Data management and tiering with EDB Postgres Distributed (PGD)#

  • Understanding Tiered Tables with EDB Postgres (coming soon)

Underlying engine components#

  • The role of PGAA and PGFS in EDB’s Lakehouse (coming soon)

  • Vectorized query execution with Apache DataFusion in EDB Postgres (coming soon)

Analytics Accelerator concepts and technologies#

Understanding analytics within EDB Hybrid Manager (HM)#

How EDB’s analytics technologies are implemented and managed in Hybrid Manager.

Key HM analytics documentation entry points#

AI/ML workloads and interoperability#

The Analytics Accelerator supports general-purpose analytics and can also serve as a platform component in AI/ML pipelines:

  • Lakehouse nodes provide efficient access to large datasets used in model training.

  • Tiered data patterns and ELT pipelines can stage data for AI/ML processing.

For AI/ML-specific concepts, see AI Factory concepts .

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