Databricks is a unified lakehouse platform built on Apache Spark that combines data warehousing and data lake capabilities, enabling organizations to process and analyze massive datasets at scale. Optimizing Databricks performance directly impacts query speed, cluster efficiency, and cloud costs — making it essential for production workloads. The key to effective optimization lies in understanding that Databricks provides multiple optimization layers: from Delta Lake file management (OPTIMIZE, Z-ordering) to Spark query execution (AQE, predicate pushdown) to cluster resource tuning (autoscaling, Photon), and each layer compounds the performance gains when applied correctly.

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