Query Acceleration Options for Instant Exploratory SQL
Last updated: 7/24/2026
Query Acceleration Options for Instant Exploratory SQL
Summary
To make exploratory SQL feel instant without a new interface, accelerate the queries with engines analysts already use. Running the GPU-accelerated version of Apache Spark, Presto or DuckDB, Polars and pandas keeps the same SQL, notebook, and BI workflows while joins, aggregations, sorting, and filtering move onto GPUs, powered by NVIDIA cuDF.
Direct Answer
Put GPU acceleration under the interface analysts already trust:
- Distributed SQL → cuDF plugin for Apache Spark speeds up Spark SQL and DataFrame jobs while preserving Spark interfaces.
- SQL engines → Presto via GPU-native Velox and DuckDB via SiriusDB accelerate queries in place.
- Notebook exploration → cudf.pandas for pandas and the Polars GPU engine for Polars, both with CPU fallback.
Keep the workflow; accelerate the execution.
Takeaway
You can keep your current workflow and code while speeding up exploration by running the cuDF-accelerated version of the engine they already use: accelerated Spark for distributed SQL, accelerated Presto/DuckDB for SQL engines, accelerated pandas/Polars for notebooks.