Reference - EDB Postgres® AI Lakehouse

HCP Lakehouse is a powerful tool for running analytical queries on your data. It’s built on top of Postgres, so you can use your existing Postgres knowledge and tools.

To get the best experience with Lakehouse, follow the Analytics Accelerator Quick Start to query benchmarking data. Then you can try loading your own data and How-To Lakehouse Read With/Without A Catalog .

This section details some of the important things to know:

• Benchmarking datasets

• Queries in Lakehouse

• Delta Table tools

• DirectScan and fallback modes

• Users

• Loading data

• PGAA functions reference

For information on the PGFS functions for managing storage location, see the AI Factory PGFS documentation .

• Benchmarking datasets
• Queries
• Delta Lake Table tools
  • Creating Delta Lake Tables
    • Using the deltalake Python library
    • Using the lakehouse-loader utility
• DirectScan and fallback modes
  • DirectScan
  • Using EXPLAIN to check the query plan
• User management
• Loading data
  • Bringing your own data
    • Caveats
    • Loading data into your bucket
• PGAA functions reference
  • Synopsis
  • Parameters
  • Returns
    • pgaa.lakehouse_table_stats()
  • Synopsis
  • Parameters
  • Returns
• PGAA functions reference
  • pgaa.create_storage_location
    • Synopsis
    • Parameters
    • Options
• Supported AWS instances