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  1. Skills Directory
  2. PySpark

PySpark

Advanced

Apache Spark is an open-source distributed processing framework for large-scale data processing.

This competency area includes designing production-grade PySpark pipelines, resolving slowly changing dimensions, performing point-in-time joins, handling event-time and late-arriving data, building windowed and sessionized aggregations, reconciling mutable event streams, optimizing Spark execution plans, handling shuffle and skew, enforcing deterministic and idempotent processing, validating complex schemas, and producing reliable analytical outputs.

Key Competencies:

  1. Model slowly changing dimensions - Build non-overlapping effective-dated histories, resolve conflicting updates, and apply latest-write-wins or deterministic tie-breaking rules. Applicable for Developer, Data Engineer.

  2. Perform point-in-time joins - Join fact/event records to the dimension values valid at the event timestamp while preserving unmatched or late-arriving records according to defined rules. Applicable for Developer, Data Engineer.

  3. Handle event-time processing and watermark logic - Use event timestamps, processing order, and watermark-like thresholds to classify records and control when results are considered complete. Applicable for Developer, Data Engineer.

  4. Process late and out-of-order data - Classify records as on-time, late-but-allowed, or too-late, and route invalid or unrecoverable records to dead-letter outputs. Applicable for Developer, Data Engineer, and Operations.

  5. Compute advanced windowed aggregations - Build tumbling, sliding, and session-style aggregations, including updates from late-but-allowed records. Applicable for Developer, Data Engineer.

  6. Sessionize activity streams - Use gaps-and-islands logic to group events into sessions, calculate session-level metrics, and close sessions based on time gaps or watermark advancement. Applicable for Developer, Data Engineer.

  7. Reconcile mutable and corrective event streams - Apply updates, corrections, deletions, voids, or reversals using deterministic ordering and business rules to compute final state or net metrics. Applicable for Developer, Data Engineer.

  8. Analyze and optimize Spark execution plans - Use explain, physical plans, caching, partitioning, broadcast joins, and adaptive execution concepts to identify and reduce expensive operations. Applicable for Developer, Data Engineer.

  9. Handle shuffle, skew, and partitioning issues - Recognize skewed keys, reduce unnecessary shuffles, choose appropriate partitioning strategies, and design joins and aggregations for large datasets. Applicable for Developer, Data Engineer.

  10. Guarantee deterministic and idempotent processing - Use stable ordering, explicit tie-breakers, repeatable output schemas, and idempotent write patterns so reruns on the same input produce the same result. Applicable for Developer, Data Engineer, and Operations.

  11. Validate complex schemas and output contracts - Detect missing nested fields and required columns, preserve schemas for empty inputs, and produce deterministic, well-ordered analytical reports. Applicable for Developer, Data Engineer.