Making it easy to interface with Chalk’s query engine, with Chalk SQL and Chalk Dataframe

Chalk introduces SQL and DataFrame interfaces for its federated query engine, designed to help ML and AI teams query, transform, and serve data spread across databases, warehouses, streaming systems, APIs, and external services without manually managing data movement. Chalk SQL offers a declarative approach to combining sources such as Postgres and BigQuery, while the Python-oriented Chalk DataFrame library supports iterative workflows familiar to pandas and Polars users; both compile into the same optimized execution plan. The engine can invoke external functions and services, including tools such as Jev, while pushing compatible filters, limits, and projections down to underlying sources to reduce scanning and data transfer. Underneath, Chalk uses and extends Meta’s Velox C++ execution engine with custom operators and integrations for more than 10 databases, aiming to support both high-throughput analytics and low-latency feature serving. By sharing a unified planning and execution model across SQL, DataFrames, and supported Python feature logic, Chalk seeks to shorten the path from exploratory analysis to production inference and agent-context workloads.

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