Analytics Accelerator

• Introduction to EDB Postgres® Analytics Accelerator
  • Navigating the analytics accelerator
  • Conceptual foundations
  • EDB core analytics technologies
  • Use Anywhere (Manual/Reference)
  • Use With PGD (Manual/Reference)
  • Use In Hybrid Manager (Manual/Reference)
  • How-Tos (Runbook-Aligned)
  • Where to start
    • Level 100
    • Level 200
    • Level 300
• Getting Started
  • Analytics Generic Concepts
    • Architectures
    • Key technologies
    • Core concepts
    • Additional concepts
    • Explore next
  • Analytics Accelerator Concepts
    • EDB’s vision for analytics on Postgres
    • Key EDB solutions and technologies
    • Analytics and AI/ML workloads
    • How EDB implements analytics across products
    • Explore next
  • Analytics Terminology
    • Apache Iceberg
    • Delta Lake
    • Data Lakehouse
    • EDB Postgres Lakehouse Cluster
    • EDB Postgres Distributed (PGD)
    • Tiered Tables
    • PGAA (Analytics Accelerator)
    • PGFS (Postgres File System)
    • AutoPartition
    • BDR Analytics Table
    • Iceberg Catalog
    • Lakekeeper
    • Open Table Formats
    • Vectorized Query Engine
    • Data Tiering
    • Separation of Storage and Compute
    • Next steps
  • Analytics Accelerator Architecture
    • Architecture Overview
    • Core Components
    • Architectural Layers
    • Data Flow Patterns
    • Performance Architecture
    • High Availability and Resilience
    • Security Architecture
    • Deployment Patterns
  • Analytics Accelerator Quick Start
    • Introduction
    • Getting started
    • Operating a Lakehouse node
    • Inspect the benchmark datasets
    • Query the benchmark datasets
  • Reference - EDB Postgres® AI Lakehouse
    • Benchmarking datasets
    • Queries
    • Delta Lake Table tools
    • DirectScan and fallback modes
    • User management
    • Loading data
    • PGAA functions reference
    • PGAA functions reference
    • Supported AWS instances
• Use Anywhere
  • EDB Postgres Lakehouse Architecture
    • What is EDB Postgres Lakehouse
    • Who Benefits from Lakehouse Architecture
    • How to Deploy Lakehouse Architecture
    • Architecture Components
    • Performance Characteristics
    • Integration Patterns
    • Operational Considerations
  • Apache Iceberg Integration With Analytics Accelerator
    • What is Apache Iceberg
    • Who Uses Iceberg Integration
    • How to Use Iceberg with Analytics Accelerator
    • Understanding Iceberg Architecture
    • Schema Evolution Capabilities
    • Catalog Integration Patterns
    • Performance Optimization Strategies
    • Common Integration Patterns
    • Operational Considerations
  • Delta Lake Integration with Analytics Accelerator
    • Understanding Delta Lake in the Lakehouse Context
    • Architecture Overview
    • Delta Lake Table Format
    • Integration Capabilities
    • Practical Applications
    • Performance Optimization
    • Migration Considerations
    • Ecosystem Integration
• Use With PGD
  • Tiered Tables With Analytics Accelerator
    • What are Tiered Tables
    • Who Benefits from Tiered Tables
    • How Tiered Tables Work
    • Architecture Overview
    • Performance Characteristics
    • Common Implementation Patterns
    • Operational Considerations
• Learning
  • Analytics Accelerator learning resources
    • Documentation categories
    • Next steps
  • Analytics Accelerator Learning Paths
    • How to use these learning paths
    • Learning path levels
    • EDB training and certification
    • Additional guidance
    • Next steps
  • Analytics Accelerator 101 Foundational concepts
    • What you will learn
    • Before you begin
    • Suggested learning path
    • Next steps
  • Analytics Accelerator 201 Practical application and core solutions
    • What you will learn
    • Before you begin
    • Suggested learning path
    • Next steps
  • Analytics Accelerator 301 Advanced techniques and optimization
    • What you will learn
    • Before you begin
    • Suggested learning path
    • Next steps
  • Analytics Accelerator for your role a persona-based guide
    • Quickly find your role
    • Database administrator (DBA)
    • DevOps engineer / site reliability engineer (SRE)
    • Data scientist / data analyst
    • Application developer
• How To Hybrid Manager
  • Create A Lakehouse Cluster
    • Why create a Lakehouse cluster?
    • Goals
    • Prerequisites
    • Steps
    • What you can do next
    • Related topics
• How To Playbook
  • How To Get Setup for Lakehouse analytics
  • Getting setup for Lakehouse analytics
    • Goals
    • Requirements
    • Create a Lakekeeper catalog
    • Identify your bucket URL
    • Sanity checks
    • Next steps
    • Takeaways
  • How-To Read/Write Without Catalog
    • Prerequisites
    • Setup: Configure Analytics Infrastructure
    • Read-Only Analytics: Access Existing Data
    • Transactional Table Analytics Offload
    • Advanced Table Management Operations
    • AutoPartition with Analytics Offload
    • Troubleshooting and Validation
  • How-To Integrate with Third-Party Iceberg Catalogs
    • Overview
    • Context and Goals
    • Prerequisites
    • Practical Applications
    • Configuration Instructions
    • Verification and Testing
    • Known Limitations
    • Troubleshooting
    • Next Steps
  • How-To Lakehouse Read With/Without A Catalog
    • Overview
    • Prerequisites
    • Configuration Approaches
    • AWS S3 Tables Integration
    • Direct Delta Lake Access
    • Direct Iceberg Access
    • Catalog Attachment
    • Query Validation
    • Best Practices
    • Troubleshooting
    • Next Steps
  • Configure Analytics Storage and Data Tiering with PGAA and PGD
    • Table of Contents
    • Part 7: External Tool Integration
    • Steps Overview
    • Prerequisites
    • Setup: Configure Analytics Infrastructure
    • Read-Only Analytics: Access Existing Data
    • Transactional Table Analytics Offload
    • Advanced Table Management Operations
    • AutoPartition with Analytics Offload
    • Catalog-Based Operations
    • Troubleshooting and Validation