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:

• How-To Guides — for step-by-step tasks
• Analytics Terminology — for key terms used in this space

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

Content overview

Articles in this section progress from foundational industry concepts to EDB-specific implementations.

Foundational analytics and modern data architectures

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

Generic concepts

• 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

EDB’s vision and strategy for analytics with Postgres

How EDB makes Postgres a unified platform for operational + analytical workloads.

Analytics Accelerator concepts

• Analytics Accelerator concepts EDB’s vision, key strategies, and how the platform unifies operational and analytical data.

Additional articles (coming soon)

• How EDB extends Postgres for high-performance analytics
• EDB’s commitment to open standards in analytics

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)

Analytical use cases and architecture patterns

Explore how EDB’s analytics technologies support common business needs.

• Analytics use cases, reference architectures, and industry solutions (coming soon)
• Architectural patterns for real-time vs. batch analytics with EDB Postgres (coming soon)
• Designing for interoperability in an EDB-centric data lakehouse (coming soon)

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

• Analytics in Hybrid Manager
• Analytics concepts within Hybrid Manager

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.