Analytics Accelerator concepts

Analytics Accelerator concepts#

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

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

EDB’s vision for analytics on Postgres#

EDB implements modern analytics patterns through the Analytics Accelerator, 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

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:

Vectorized query optimization#

Analytics Accelerator 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

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:

Advanced query optimization#

Analytics Accelerator 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

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:

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 (Postgres Generic Analytics Adapter) 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

Explore next#

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