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Open-Source Tool Processes 25 Million Rows in 5 Seconds on Consumer Hardware

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A capability benchmark using the Interlace data pipeline framework processed 25 million synthetic rows through a 12-model DAG in approximately five seconds on a consumer-grade AMD Ryzen 9 laptop with 60 GB RAM. The test ran on DuckDB 1.5.4 with Python 3.12.3, using six different data materialization strategies including incremental, merge, and slowly changing dimensions. Because the pipeline schedules independent branches concurrently, CPU usage reached 270% utilization, compressing 13.8 seconds of total CPU work into just over 5 seconds of wall-clock time. Peak memory consumption stayed under 2 GB throughout, as data was streamed via Arrow RecordBatches rather than loaded into pandas DataFrames. The benchmark is fully reproducible, with all commands publicly documented so developers can verify the results on their own machines within minutes.

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