Leveraging Analytics in Hybrid Manager
This page provides role-based guidance on how to leverage analytics features within Hybrid Manager (HM).
Whether you are a Database Administrator, DevOps Engineer, Data Scientist, or Application Developer, Hybrid Manager provides tools to help you manage, analyze, and optimize your EDB Postgres deployments.
For key concepts, see Analytics Concepts in Hybrid Manager.
Database Administrator (DBA)
Primary focus: Performance, reliability, and cost-efficiency of HM-managed Postgres environments.
Key objectives and HM features
- Monitor PGD clusters and Lakehouse nodes using HM dashboards.
- Implement Tiered Tables on PGD to offload cold data to object storage.
- Analyze trends and scale Lakehouse clusters as needed.
- Manage backup and recovery for PGD and Lakehouse metadata (including catalogs).
- Control access to Lakehouse clusters through HM and Postgres security features.
Key links
- Lakehouse Clusters in Hybrid Manager
- Implementing Tiered Tables in Hybrid Manager
- Working with Apache Iceberg in Hybrid Manager
- Performance analytics dashboards in Hybrid Manager
DevOps Engineer / SRE
Primary focus: Automating deployment and observability of analytics infrastructure.
Key objectives and HM features
- Use HM UI, API, or CLI to deploy PGD clusters and Lakehouse Clusters.
- Adjust Lakehouse Cluster size as needed via HM.
- Monitor Lakehouse and PGD performance in HM dashboards.
- Automate provisioning and configuration through HM API/CLI.
- Optimize cost using separate compute/storage architecture.
Key links
- Lakehouse Clusters in Hybrid Manager
- Implementing Tiered Tables in Hybrid Manager
- Working with Apache Iceberg in Hybrid Manager
- HM API and CLI documentation
Data Scientist / Data Analyst
Primary focus: Efficiently accessing and analyzing data managed by Hybrid Manager.
Key objectives and HM features
- Connect BI tools or analytical tools (Jupyter, R, SQL clients) to Lakehouse nodes.
- Query Iceberg and Delta Lake tables via Lakehouse.
- Use vectorized Lakehouse nodes for fast analytics on large datasets.
- Prepare and clean data using Postgres SQL on Lakehouse or PGD.
Key links
- Lakehouse Clusters in Hybrid Manager
- Working with Apache Iceberg in Hybrid Manager
- Working with Delta Lake in Hybrid Manager
- Implementing Tiered Tables in Hybrid Manager
Application Developer
Primary focus: Building applications that interact with data managed by Hybrid Manager.
Key objectives and HM features
- Connect applications to Lakehouse nodes or PGD read replicas for embedded reporting.
- Route complex analytical queries to Lakehouse nodes.
- Manage time-series data across hot (PGD) and cold (Lakehouse) tiers using Tiered Tables.
- (Optional) If using Gen AI Builder with HM, integrate Griptape Structures/Tools with applications.
Key links
For an overview of core analytics concepts, see Analytics Concepts in Hybrid Manager.
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