Analytics Accelerator Concepts

Analytics Accelerator Concepts#

This page explains how EDB delivers modern analytics capabilities through the Analytics Accelerator (PGAA) and Hybrid Manager (HM).

This page is part of the Analytics Hub. For full navigation, visit: Analytics Hub — Analytics Accelerator Concepts — How-Tos (Runbook-Aligned)

It builds on established industry patterns and technologies. If you’re new to the space, start with:

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EDB’s vision for analytics on Postgres#

EDB implements modern analytics patterns through the Analytics Accelerator (PGAA), enabling Postgres to serve as a unified platform for both operational and analytical workloads.

Key principles:

  • Perform analytics close to operational data, reducing data movement and latency

  • Use open formats and vectorized query engines for fast analytics on object storage

  • Support data tiering to balance cost and performance

  • Separate compute and storage to allow independent scaling

  • Manage the entire stack via Hybrid Manager

Related concept: Data lakehouse

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Key EDB solutions and technologies#

EDB Postgres Lakehouse#

Lakehouse architecture enables EDB Postgres to query data in object storage:

  • Lakehouse nodes query data in open table formats (Iceberg, Delta Lake)

  • Vectorized query execution powered by Apache DataFusion

  • Columnar storage formats such as Parquet

  • Separation of compute and storage

Related concepts:

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Vectorized query optimization#

Analytics Accelerator (PGAA) embeds Apache DataFusion in Lakehouse nodes:

  • Processes data as columnar batches

  • Uses SIMD instructions to accelerate performance

  • Optimized for Parquet-formatted data

Related concept: Vectorized query engines

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EDB Postgres Distributed (PGD) and Tiered Tables#

EDB Postgres Distributed (PGD) powers Tiered Tables:

  • BDR AutoPartition manages time-based partitioning

  • Cold data is automatically offloaded to object storage (Iceberg tables)

  • Hot data remains in transactional PGD nodes

This balances operational performance with cost-efficient historical data access.

Related concepts:

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Advanced query optimization#

Analytics Accelerator (PGAA) improves core Postgres capabilities for analytics:

  • Enhanced parallel query processing

  • Optimized join algorithms for analytical workloads

  • Tight integration with vectorized engines for high-performance OLAP queries

Related concept: OLAP / OLTP

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Analytics and AI/ML workloads#

Analytics Accelerator supports traditional analytics and AI/ML data pipelines:

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

  • Tiered data pipelines support cost-effective AI/ML staging patterns

  • Open table formats enable integration with vector search and retrieval augmented generation (RAG) workflows

Related concepts:

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How EDB implements analytics across products#

Hybrid Manager (HM)#

Hybrid Manager acts as the control plane:

  • Provision and manage Lakehouse clusters

  • Configure and monitor Tiered Tables

  • Manage storage locations, catalog connections, and compute nodes

EDB Postgres Advanced Server / Extended Server#

EDB Postgres Advanced Server adds:

  • Improved parallel query capabilities

  • Advanced SQL features helpful for analytics

  • Native support for PGAA features

EDB Postgres Distributed (PGD)#

PGD provides the foundation for:

  • High-availability transactional workloads

  • Data tiering and offloading (Tiered Tables)

  • Scalable architectures that integrate with Lakehouse clusters

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Explore next#

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