Solving Business Problems with Analytics in Hybrid Manager

This page explores common analytics use cases and explains how Hybrid Manager (HM) features can help you address them.

By using EDB Postgres Lakehouse Clusters, Tiered Tables with PGD, and integrations with open table formats (Apache Iceberg, Delta Lake), you can build flexible analytics solutions within your Postgres environment.

For key concepts, see Analytics Concepts in Hybrid Manager.

For persona-based guidance, see Persona-Based Guide to Using Analytics in Hybrid Manager.

Common use cases and HM solutions

Ad-hoc business intelligence and reporting on large datasets

Challenge: Business users and data analysts need to run complex queries on large historical datasets without affecting operational PGD clusters.

HM solution:

• Store large datasets in object storage as Iceberg or Delta Lake tables.
• Provision Lakehouse Clusters for fast, vectorized query execution.
• Connect BI tools (Tableau, Power BI, Looker) to Lakehouse Clusters using standard Postgres connectors.
• Use an Iceberg catalog (HM-managed or external) for table discovery and management.

Learn more: Lakehouse Clusters in Hybrid Manager Working with Apache Iceberg in Hybrid Manager

Cost-effective archival and analysis of time-series data

Challenge: Manage growing time-series data cost-effectively while maintaining access for historical analysis.

HM solution:

• Configure Tiered Tables with PGD AutoPartition and analytics_offload_period for automatic offload of cold partitions to Iceberg.
• Query cold data in object storage using Lakehouse Clusters.
• Support transparent access to both hot and cold data through PGD parent tables.

Learn more: Implementing Tiered Tables in Hybrid Manager

Integrating operational data with an enterprise data lake

Challenge: Combine data from PGD clusters with other enterprise data sources for unified analytics.

HM solution:

• Offload PGD data to object storage in Iceberg or Delta formats.
• Use a shared Iceberg catalog (HM-managed or external) to support interoperability.
• Query shared Iceberg or Delta tables using Lakehouse Clusters or external engines (Spark, Trino).

Learn more: Working with Apache Iceberg in Hybrid Manager Lakehouse Clusters in Hybrid Manager

Accelerating analytical components of applications

Challenge: Applications require analytical queries that could impact primary PGD cluster performance.

HM solution:

• Route read-heavy analytical queries to dedicated Lakehouse Clusters.
• Precompute aggregates in Lakehouse tables or materialized views for faster application queries.
• Use Tiered Tables to manage time-series data efficiently across hot and cold storage.

Benefits:

• Protect PGD performance.
• Provide scalable compute for application analytics.
• Leverage cost-effective storage.

Industry examples and tutorials

Financial services

• Building a daily risk reporting system using Lakehouse and offloaded PGD data
• Implementing Tiered Tables for long-term trade data archival and analysis

Retail and e-commerce

• Analyzing customer purchase history with Lakehouse and BI tool integration

Telecommunications

• Call Detail Record (CDR) analysis using Tiered Tables and Lakehouse Clusters

Next topic

Persona-Based Guide to Using Analytics in Hybrid Manager