Analytics Accelerator tutorials

Use this section to explore in-depth, step-by-step tutorials for building practical solutions with the Analytics Accelerator.

Tutorials are learning-oriented and guide you through achieving meaningful outcomes. They often cover multiple features and concepts working together to solve larger problems.

For concise, goal-oriented instructions on specific tasks, see the How-To guides. For tutorials specific to Hybrid Manager (HM), refer to the HM analytics tutorials.

Tutorial categories

Getting started and foundational projects

Tutorials for users new to EDB’s advanced analytics capabilities or those building foundational projects.

Tutorials

Building your first EDB Postgres Lakehouse - a beginner's guide (self-managed focus) Level: Beginner Estimated time: ~45 min Products used: Postgres Lakehouse, PGAA, PGFS End result: Stand up a Postgres Lakehouse node and query object storage from Postgres. Related concepts: Generic concepts - data lakehouse, Analytics Accelerator concepts

From raw data to queryable lakehouse table - an end-to-end example Level: Beginner Estimated time: ~1 hr Products used: Postgres Lakehouse, PGFS, Delta Lake End result: Load raw data into object storage and query it as a Lakehouse table. Related concepts: Generic concepts - separation of storage and compute

Setting up and querying a basic multi-source analytical view in EDB Postgres Level: Intermediate Estimated time: ~1.5 hrs Products used: PGAA, PGFS, PGD, Postgres Lakehouse End result: Combine multiple data sources into a unified analytical view. Related concepts: Analytics Accelerator concepts

End-to-end EDB Postgres Lakehouse implementations

Projects that demonstrate full Lakehouse workflows — ingestion, storage, querying.

Tutorials

Building a sales analytics lakehouse with EDB Postgres, Iceberg, and object storage Level: Intermediate Estimated time: ~2 hrs Products used: Postgres Lakehouse, PGAA, Iceberg REST catalog, BI tools End result: Build an analytical pipeline and visualize Lakehouse data in BI tools. Related concepts: Generic concepts - open table formats

Migrating and analyzing historical data with EDB Postgres Lakehouse and Delta Lake Level: Intermediate Estimated time: ~2-3 hrs Products used: Postgres Lakehouse, PGFS, Delta Lake End result: Migrate and analyze historical data sets using Delta format. Related concepts: Analytics Accelerator concepts

Advanced data exploration and visualization with EDB Postgres Lakehouse and BI tools Level: Advanced Estimated time: ~2-3 hrs Products used: Postgres Lakehouse, PGAA, BI tools (Tableau, PowerBI, Superset) End result: Build an interactive analytical dashboard on Lakehouse data. Related concepts: Analytics Accelerator concepts

Advanced PGD for analytics and tiered storage

Using PGD for tiering, offloading, and hybrid analytical architectures.

Tutorials

Implementing a full tiered storage solution with PGD AutoPartition and Iceberg catalog offload Level: Advanced Estimated time: ~3 hrs Products used: PGD, AutoPartition, Iceberg catalog, Postgres Lakehouse End result: Configure automatic tiering and offload partitions to Iceberg. Related concepts: Generic concepts - data tiering

Performance optimization for queries spanning hot and cold tiers in a PGD Lakehouse setup Level: Advanced Estimated time: ~2-3 hrs Products used: PGD, PGAA, Postgres Lakehouse End result: Tune and test queries across tiered storage layers. Related concepts: Analytics Accelerator concepts

Managing the lifecycle of offloaded data in a PGD tiered table environment Level: Advanced Estimated time: ~2 hrs Products used: PGD, AutoPartition, Postgres Lakehouse End result: Implement data retention and lifecycle management policies for tiered tables. Related concepts: Analytics Accelerator concepts

Building AI-powered applications with Gen AI Builder and analytics

Using Gen AI Builder + Analytics Accelerator together.

For in-depth AI/ML concepts, see the AI Factory concepts.

Tutorials

Developing and deploying a PG Financial Inquiry Router structure Level: Intermediate Estimated time: ~1 hr Products used: Gen AI Builder, Griptape, Postgres Lakehouse End result: Build a Griptape structure that routes user inquiries to analytical data. Related concepts: Analytics Accelerator concepts

Developing and deploying a PG Financial Account Balance Tool Level: Intermediate Estimated time: ~1 hr Products used: Gen AI Builder, Griptape, Postgres Lakehouse End result: Build a Griptape tool to query account balances from Lakehouse data. Related concepts: Analytics Accelerator concepts

Creating an AI assistant that uses a custom Griptape tool to query an EDB Postgres Lakehouse Level: Advanced Estimated time: ~2 hrs Products used: Gen AI Builder, Griptape, Postgres Lakehouse, PGAA End result: Implement an AI assistant that can query Lakehouse data on demand. Related concepts: Analytics Accelerator concepts

Industry solution walkthroughs

End-to-end tutorials for solving real-world business problems with Analytics Accelerator.

Tutorials

Real-time fraud detection analytics using EDB Postgres and streaming data Level: Advanced Estimated time: ~2-3 hrs Products used: PGD, PGAA, Lakehouse, streaming engine (Kafka, Flink) End result: Build a real-time fraud detection pipeline using Lakehouse + streaming. Related concepts: Analytics Accelerator concepts

Building a regulatory reporting data mart with EDB Postgres Lakehouse and tiered tables Level: Intermediate Estimated time: ~2 hrs Products used: PGD, AutoPartition, Lakehouse, BI tools End result: Implement a reporting data mart architecture using tiered tables. Related concepts: Generic concepts - OLAP/OLTP

This index will grow as additional tutorials are added. For Hybrid Manager (HM)-specific tutorials, see the HM analytics tutorials.