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AIP Virtual Tables

1 post · Jun 2024

What this trail covers

This post introduces AIP Virtual Tables, a new capability within Palantir's Artificial Intelligence Platform (AIP) that allows for the creation and querying of virtual tables backed by external data sources without requiring data ingestion into Foundry. This enables real-time access to data residing in systems like Snowflake, Databricks, and cloud object storage, facilitating complex analytical queries and AI agent interactions directly on the source data. The system leverages a query federation layer that translates AIP queries into native queries for the underlying data stores, returning results to AIP for further processing. This approach significantly reduces data duplication, improves data freshness, and simplifies data management by allowing users to define virtual tables that map to existing datasets.

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  1. MediumThis post details the technical implementation of AIP Virtual Tables. It describes the architecture, which includes a query federation layer that translates AIP queries into native SQL or object storage queries. The system supports defining virtual tables with schemas that map to external data sources, enabling direct querying without data movement. It highlights the use of a query planner and executor that interacts with various data connectors (e.g., Snowflake, S3, Databricks) to retrieve data. The post also touches upon performance considerations, such as optimizing query translation and handling data retrieval efficiently to maintain a responsive user experience for AI agents and analytical workloads.