Forces replay-safe rollout plans, reconciliation gates, and rollback paths before executing any data backfill or historical reprocessing. Use when running /backfill, rerunning pipelines, repairing publish windows, or any work that risks double-counting or downstream corruption.
Diagnose slow, expensive, or regressed Apache Spark and PySpark applications by comparing runtime evidence against a healthy run. Use for long stages, stragglers, skew, shuffle, spill, garbage collection, poor parallelism, small files, slow scans, scheduler delay, executor imbalance, and unexplained compute-cost…
Knowledge base from "Learning Spark, 2nd Edition" by Damji, Wenig, Das & Lee. Use when working with Apache Spark / PySpark, preparing for the Databricks Certified Associate Developer for Apache Spark exam, or applying the authors' frameworks for Spark architecture, DataFrame API, Spark SQL, Structured Streaming…
★not rated 13 4mo agoA77 tokens
originalMIT
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