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#
** Analytics/Lakehouse ** 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: Data lakehouse , EDB Postgres Lakehouse
** Analytics/Lakehouse ** 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: Separation of storage and compute
** Analytics/Lakehouse ** 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#
** Analytics/Lakehouse ** 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: Open table formats
** Analytics/Lakehouse ** 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: EDB Postgres Lakehouse
** Analytics/Lakehouse ** Level: Advanced Estimated time: ~2-3 hrs Products used: Postgres Lakehouse, PGAA, BI tools (Tableau, PowerBI) 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#
** Analytics/Lakehouse ** 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: Data tiering
** Analytics/Lakehouse ** 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
** Analytics/Lakehouse ** 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#
** Analytics/Lakehouse ** 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
** Analytics/Lakehouse ** 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
** Analytics/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#
** Analytics/Lakehouse ** 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
** Analytics/Lakehouse ** 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: OLAP / OLTP
This index will grow as additional tutorials are added. For Hybrid Manager (HM)-specific tutorials, see the HM analytics tutorials .