Analytics Accelerator for your role: a persona-based guide

Use this guide to understand how to leverage EDB Postgres® AI and analytics capabilities for your specific role. EDB Postgres AI provides a powerful platform for a wide range of analytical needs. This guide helps you focus on the capabilities that align with your goals.

Quickly find your role

• Database administrator (DBA)
• DevOps engineer / site reliability engineer (SRE)
• Data scientist / data analyst
• Application developer

For details on how to implement and manage these solutions within EDB Hybrid Manager (HM), see Analytics in EDB Hybrid Manager (HM spoke root).

Database administrator (DBA)

Focus on performance, reliability, cost efficiency, and manageability of your database infrastructure.

Key objectives and Analytics Accelerator solutions

Performance monitoring and tuning

Identify slow queries, bottlenecks, and inefficient resource usage. Use HM dashboards to monitor performance of EDB Postgres Lakehouse Nodes and PGD clusters. HM performance monitoring.

Storage management and cost optimization

Manage large databases and historical data cost effectively. Use tiered tables with PGD and EDB Postgres Lakehouse to offload cold data to object storage (as Iceberg tables). Understanding tiered tables with EDB Postgres. Manage tiered tables via HM.

Capacity planning

Forecast storage and compute needs. Analyze historical growth patterns and current utilization via Lakehouse and HM dashboards. Leverage the separation of compute and storage in Lakehouse for flexible scaling.

Backup and recovery of analytical data

Protect data in object storage and associated metadata. Follow Iceberg catalog backup strategies and HM backup options. HM backup and recovery for Lakehouse components.

Ensuring data accessibility for analytics

Provide performant and secure access to data analysts and data scientists. Use Lakehouse Nodes and PGD offloading to minimize impact on transactional systems.

Recommended resources

• EDB Postgres Distributed (PGD) documentation
• EDB Postgres Lakehouse overview
• Analytics in EDB Hybrid Manager

DevOps engineer / site reliability engineer (SRE)

Focus on automation, infrastructure management, scalability, reliability, and observability.

Key objectives and Analytics Accelerator solutions

Automated provisioning and management

Use HM control plane to provision Lakehouse clusters and related resources via UI, API, or CLI. Provisioning Lakehouse clusters on HM.

Scalability and elasticity

Independently scale Lakehouse compute resources to meet demand.

Monitoring and observability

Gain visibility into performance, health, and cost of analytical systems. Use HM dashboards and logging. HM monitoring for analytics.

CI/CD for data pipelines and analytical applications

Integrate schema changes (Iceberg evolution) and data offloading via PGD into CI/CD pipelines.

Cost management for cloud resources

Track and optimize costs for object storage and analytical compute. Use HM resource insights.

Recommended resources

• Analytics in EDB Hybrid Manager
• EDB Postgres Lakehouse detailed concepts
• EDB API and CLI documentation

Data scientist / data analyst

Focus on extracting insights, building models, and answering business questions.

Key objectives and Analytics Accelerator solutions

Accessing large and diverse datasets

Query large datasets stored as Iceberg or Delta Lake tables via Lakehouse Nodes. Use Python, R, SQL clients, and BI tools.

Data preparation and exploration

Perform data filtering, aggregation, and transformation with SQL via Lakehouse Nodes.

Working with open table formats

Leverage schema evolution, time travel, and ACID transactions in Iceberg and Delta Lake tables. Understanding Apache Iceberg with EDB solutions. Understanding Delta Lake with EDB solutions.

Integration with data science tools and frameworks

Load Lakehouse query results into pandas, R data frames, or use with Spark, TensorFlow, or PyTorch.

Query performance

Accelerate analytical SQL queries with vectorized query engines in Lakehouse Nodes.

Recommended resources

• EDB Postgres Lakehouse overview
• Tutorial: Querying Delta Lake data
• Tutorial: Querying Iceberg tables from HM Lakehouse Node
• Connecting BI tools

Application developer

Focus on building data-driven applications and embedding insights.

Key objectives and Analytics Accelerator solutions

Building applications with analytical features

Use Lakehouse Nodes or PGD read replicas to provide reporting, dashboards, and data exploration in applications.

Efficiently handling large reporting queries

Offload large aggregation and historical data queries to Lakehouse Nodes. Use tiered tables to direct historical queries to an analytical tier.

Accessing data from a unified platform

Provide consistent SQL access to both operational and analytical data.

Developing AI/ML-enabled applications

Store and access ML model data using Lakehouse Nodes.

Recommended resources

• Postgres SQL documentation and client libraries
• EDB Postgres Lakehouse overview
• Understanding tiered tables with EDB Postgres

Explore these resources for more detailed guidance. For HM-specific implementation steps, see Analytics in EDB Hybrid Manager (HM spoke root).