Analytics in Hybrid Manager v1.3

Welcome to the central resource for understanding and leveraging the analytics capabilities embedded within EDB Hybrid Manager (HM).

Hub quick links: Analytics Hub — Concepts — How-Tos

Hybrid Manager provides integrated support for:

• Apache Iceberg and catalog services
• Delta Lake querying
• Tiered Tables with EDB Postgres Distributed (PGD)
• Lakehouse Clusters for high-speed analytics on object storage
• Unified management and observability of your analytics stack

By using these capabilities, you can build modern, cost-effective analytics architectures on Postgres with reduced operational overhead—fully managed through Hybrid Manager.

Why Analytics in Hybrid Manager

EDB Hybrid Manager integrates analytics to help you:

• Gain unified visibility: View key metrics and operational trends across your entire EDB Postgres fleet managed by HM.
• Optimize performance: Identify performance bottlenecks and understand query behavior within HM-managed databases and Lakehouse Clusters.
• Manage resources efficiently: Monitor and tune resource consumption for HM-managed instances, including analytical workloads.
• Enhance operational health: Track the availability and status of analytical components and underlying database infrastructure.
• Simplify data architectures: Provision and manage EDB Postgres Lakehouse Clusters, implement Tiered Tables with PGD, and integrate with object storage—all from HM.

Core Concepts and Capabilities

Apache Iceberg

In Hybrid Manager, Iceberg is used to:

• Manage large analytical tables in Iceberg format, queried by Lakehouse Clusters.
• Support PGD offloading of cold partitions to object storage in Iceberg format.
• Enable interoperability with external tools (Spark, Trino, etc.) via catalog-managed tables.
• Facilitate catalog management via HM-managed services (Lakekeeper) or external catalogs.

Working with Apache Iceberg in Hybrid Manager

Next step: Configure an Iceberg catalog connection

Delta Lake

Hybrid Manager supports querying Delta Lake tables:

• Lakehouse Clusters can query existing Delta Lake tables stored in object storage.
• Integrate HM-managed Lakehouse with Delta-based pipelines (Spark, Databricks, etc.).
• Enable fast analytics on existing Delta data lakes via Lakehouse Clusters.

Working with Delta Lake in Hybrid Manager

Next step: Read Iceberg/Delta with or without a catalog

Tiered Tables with PGD

Tiered Tables enable efficient management of large time-series datasets:

• PGD’s AutoPartition automates time-based partitioning.
• Cold partitions are offloaded to Iceberg format in object storage.
• Lakehouse Clusters provide query access across hot and cold data.
• Hybrid Manager helps configure and monitor Tiered Tables end-to-end.

Implementing Tiered Tables in Hybrid Manager

Next step: Tiered Tables concepts

Lakehouse Clusters

Lakehouse Clusters are managed compute nodes in HM:

• Provide scalable, vectorized query execution on object storage.
• Support both Iceberg and Delta Lake formats.
• Integrate with catalogs (HM-managed or external).
• Unified with the broader HM ecosystem (PGD offloading, Tiered Tables, BI integrations).

Lakehouse Clusters in Hybrid Manager

Next step: Create a Lakehouse cluster

How These Capabilities Work Together

• Data offloading: PGD clusters offload cold data to object storage in Iceberg format.
• Catalog services: HM integrates with catalogs to manage table metadata; Lakehouse and PGD query through these catalogs.
• Lakehouse Clusters: Managed compute nodes run scalable analytical queries on offloaded data.
• Delta Lake integration: Lakehouse Clusters also query existing Delta Lake tables in place.
• Unified management: Hybrid Manager provides a single control plane to provision, configure, monitor, and optimize your analytics architecture.

Getting Started: Quickstart Checklist

Follow this checklist to begin applying Hybrid Manager analytics features:

• Analytics concepts (hub) — Understand core patterns and capabilities.
• Create a Lakehouse cluster — Deploy Lakehouse Clusters to enable scalable querying on object storage.
• Configure an Iceberg catalog connection — Connect HM-managed or external catalogs to manage Iceberg tables.
• Read with or without a catalog — Query Iceberg and Delta Lake tables from your Lakehouse Clusters.
• Read/write without a catalog — Direct file-based access for development and testing.
• Explore Persona-Based Guidance — Tailored recommendations for DBAs, DevOps/SREs, Data Scientists, and Developers.
• Explore architectures and examples — Concepts and patterns to solve common analytics problems.
• Analytics How-To Index — Step-by-step guides for configurations and operations.

Explore Further

• Analytics concepts (hub)
• Working with Apache Iceberg in Hybrid Manager
• Working with Delta Lake in Hybrid Manager
• Implementing Tiered Tables in Hybrid Manager
• Lakehouse Clusters in Hybrid Manager
• Persona-Based Guide
• Architectures and examples
• Analytics How-To Index

By leveraging these capabilities, Hybrid Manager enables you to build a modern, flexible, and performant analytics architecture across your EDB Postgres deployments—all through a unified and operationally efficient experience.