Analytics Accelerator for your role a persona-based guide

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#

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

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.

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). Tiered Tables With Analytics Accelerator . 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.

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.

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 Hybrid Manager to provision Lakehouse clusters and related resources via the console, API, or CLI. Create A Lakehouse Cluster .

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.

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.

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. Apache Iceberg Integration With Analytics Accelerator . Delta Lake Integration with Analytics Accelerator .

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.

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.

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