Cost and query optimization guides for modern data workloads
Practical guides for data engineers to cut compute cost and speed up queries at scale, on whichever engine you run today.
Primary keys, skip indices, materialized views, projections, codecs, dictionaries, and prewhere.
Read the guide →Parquet partitioning, result caching, smart joins, compaction, approximate functions, and Unload.
Read the guide →Connector pushdown, join optimization, partitioning, caching, memory management, and fault tolerance.
Read the guide →Clustering, partitioning, materialized views, BI Engine, slot reservations, and anti-patterns.
Read the guide →V-ORDER, OneLake shortcuts, Direct Lake, Eventstream, KQL Database, and CU optimization.
Read the guide →15 techniques: distribution keys, materialized views, workload management, and caching.
Read the guide →Z-ordering, Delta Cache, broadcast joins, AQE, Auto Loader, CDF, auto compaction, and serverless SQL.
Read the guide →Right-sizing and multi-cluster warehouses, exploiting the result cache with stable SQL, and modeling credits.
Read the guide →Materialized views, smart partitioning, result caching, sampling, query guards, and autoscaling.
Read the guide →Monitoring yardsticks, Power BI aggregations, Delta layout, materialization, and caching.
Read the guide →Auto-scaling warehouses, optimized Delta layout, cluster pools, and right-sized spot clusters.
Read the guide →Materialized views, intelligent sampling, dictionary lookups, and optimized batch sizes.
Read the guide →Result reuse, workgroup limits, partition projection, and compaction.
Read the guide →15 tactics, from throttling dashboard refreshes to the 24-hour result cache with stable SQL.
Read the guide →Book a demo on your own workloads
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