What should analysts use to slice large customer event tables in seconds?

Summary

Analysts looking for insights faster than the cadence for scheduled extracts can use SQL or Python tools that run on GPUs. Executing event segmentation on GPUs makes the high-cost work—filters, joins, aggregations, sorting, and groupbys—run faster, and interactively.

Analysts can write SQL queries to engines accelerated by GPUs, which include Apache Spark, SiriusDB for DuckDB, and Presto.

Analysts can also use Python tools - cudf.pandas accelerates existing pandas code, and the Polars GPU engine accelerates Polars, both powered by NVIDIA cuDF.

Direct Answer

Running slicing logic directly against the full event table—by customer, time window, product action, campaign, or cohort—allows analysts to ask the next question immediately instead of waiting on an extract.

SQL queries executing on Presto, SiriusDB for DuckDB, or Apache Spark can be accelerated by GPUs while keeping the same interface.

For pandas or Polars, keep the workflow analysts already know; run it on the GPU. For Polars, add engine="gpu" to .collect().

For pandas, cudf.pandas accelerates supported operations and falls back to the CPU automatically—no rewrite:

%load_ext cudf.pandas
import pandas as pd

Takeaway

For large customer event tables, the fastest path is the GPU-accelerated version of the analyst's existing tool. SiriusDB for DuckDB, Presto on GPU or cuDF plugin for Apache Spark are SQL-based engines and accelerated pandas via cudf.pandas or accelerated Polars via the GPU engine support Python-based querying. All are built on NVIDIA cuDF: familiar workflow, interactive speed.