Technical Discussion: Distributed SQL Engine Requirements for Ultra-Wide Tables in ML and Multi-Omics Data
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synsqlbythesea
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Summary
A technical discussion about the limitations of current SQL databases and data processing systems when handling ultra-wide tables with thousands to tens of thousands of columns, particularly in ML feature engineering and multi-omics data contexts. The author describes practical limitations where standard SQL databases cap at around 1,000-1,600 columns, columnar formats like Parquet require Spark or Python pipelines, and OLAP engines have their own constraints. The post seeks recommendations for distributed SQL engines that can handle such wide tables effectively.
Key quotes
· 4 pulledAt some point, the problem stops being 'how many rows' and becomes 'how many columns'. Thousands, then tens of thousands, sometimes more.
Standard SQL databases usually cap out around ~1,000–1,600 columns.
Columnar formats like Parquet can handle width, but typically require Spark or Python pipelines.
OLAP engines are fast, but tend to assume a certain column count range.
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