Reference - EDB Postgres® AI Lakehouse

EDB Postgres® AI Lakehouse is an early product. Eventually, it will support deployment modes across multiple clouds and on-premises. However, currently it’s fairly limited in terms of where you can deploy it and what data you can query with it.

To get the best experience with Lakehouse, you should follow the Quick Start - EDB Postgres Lakehouse to query benchmarking data. Then you can try loading your own data with Lakehouse Sync or you can bring your own data and use external tables . If you’re intrigued, reach out to us and we can talk more about your use case and potential opportunities.

This section details some of the important things you should know.

• Supported cloud providers and regions
• Supported AWS instances
• Benchmarking datasets
• Queries
• Delta Lake Table tools
  • Creating Delta Lake Tables
    • Using the deltalake Python library
    • Using the lakehouse-loader utility
• PGAA functions reference
  • pgaa.create_storage_location
    • Synopsis
    • Parameters
    • Options
• DirectScan, fallback modes, and EXPLAIN
• User management
• Loading data (sync or bring your own)
  • Loading data with Lakehouse sync
  • Bringing your own data
    • Caveats
    • Loading data into your bucket