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 ^^^^^^^^^ \*\* :ref:`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:** :ref:`Data lakehouse ` , :ref:`EDB Postgres Lakehouse ` \*\* :ref:`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:** :ref:`Separation of storage and compute ` \*\* :ref:`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:** :ref:`Analytics Accelerator concepts ` End-to-end EDB Postgres Lakehouse implementations ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Projects that demonstrate full Lakehouse workflows — ingestion, storage, querying. .. _tutorials-1: Tutorials ^^^^^^^^^ \*\* :ref:`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:** :ref:`Open table formats ` \*\* :ref:`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:** :ref:`EDB Postgres Lakehouse ` \*\* :ref:`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:** :ref:`Analytics Accelerator concepts ` Advanced PGD for analytics and tiered storage ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Using PGD for tiering, offloading, and hybrid analytical architectures. .. _tutorials-2: Tutorials ^^^^^^^^^ \*\* :ref:`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:** :ref:`Data tiering ` \*\* :ref:`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:** :ref:`Analytics Accelerator concepts ` \*\* :ref:`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:** :ref:`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-3: Tutorials ^^^^^^^^^ \*\* :ref:`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:** :ref:`Analytics Accelerator concepts ` \*\* :ref:`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:** :ref:`Analytics Accelerator concepts ` \*\* :ref:`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:** :ref:`Analytics Accelerator concepts ` Industry solution walkthroughs ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ End-to-end tutorials for solving real-world business problems with Analytics Accelerator. .. _tutorials-4: Tutorials ^^^^^^^^^ \*\* :ref:`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:** :ref:`Analytics Accelerator concepts ` \*\* :ref:`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:** :ref:`OLAP / OLTP ` -------------- This index will grow as additional tutorials are added. For Hybrid Manager (HM)-specific tutorials, see the `HM analytics tutorials `_ . .. toctree:: :maxdepth: 3