Data Engineering
Data Partitioning Strategy Reference
Search data partitioning concepts. Covers time partitioning, partition pruning, hive-style paths, high-cardinality pitfalls, file size optimization, and BigQuery clustering.
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About this tool
Data Partitioning Strategy Reference
The Data Partitioning Strategy Reference covers time partitioning, partition pruning, hive-style paths, high-cardinality pitfalls, file size guidance, and clustering vs partitioning.
• Look up time partitioning options before designing a Spark dataset
• Reference hive-style partitioning before configuring an Athena data source
• Find the small file problem threshold before designing partition granularity
• Compare partitioning vs clustering before a BigQuery schema design
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FAQ
What does this tool tell you?
The Data Partitioning Strategy Reference covers time partitioning, partition pruning, hive-style paths, high-cardinality pitfalls, file size guidance, and clustering vs partitioning.
What affects the result most?
Partition pruning: WHERE partition_col = 'value' — query reads only matching partitions. Time partitioning: partition by day/month/year — most common for event and transaction data. High-cardinality partitioning: partitioning on user_id creates millions of partitions — avoid.
How should I use the result?
Use this tool to orient quickly to the concepts, field names, or values you are about to look up in a full specification or vendor documentation. It summarizes the common cases; the authoritative source remains whichever standard or vendor doc defines the values themselves.