DataFrame or SQL: choose a local analysis workflow

Start with the work you already have

DataStore is the closer starting point for a pandas-shaped sequence of filtering, grouping and joining. The SQL skill is more direct when the team already reasons in queries or needs ClickHouse expressions. Neither choice requires adopting both interfaces for a small task. A source claiming API similarity is a reason to test an existing calculation, not proof that every pandas operation behaves identically.

Make execution visible

A lazy DataFrame expression may only construct a plan until a result is materialized. An embedded SQL query has its own output and session choices. When comparing speed, time the complete operation and inspect what data was actually read, transferred and returned. Generated SQL can be useful evidence for the DataStore route; parameterized queries are important for the SQL route.

Keep local and remote decisions separate

Both source files declare environment prerequisites, and their examples include remote connectors. Starting from a local CSV or Parquet fixture is different from authorizing a database or cloud-storage connection. Review credentials, network access, data movement and persistence before adding a remote source. Installing instruction files does not install or validate the underlying runtime.

Use a shared acceptance fixture

Run a small dataset containing nulls, repeated join keys, wide numbers and empty groups through an independent reference calculation. Compare schema, row counts and aggregates before profiling. Choose the interface that your team can explain and maintain after it satisfies those checks; this comparison does not claim a measured performance winner.