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Apache Spark: correct distributed data and recoverable pipelines

A complete Spark course joining relational correctness, distributed execution, streaming semantics and operational evidence.

18 lessonsEnglish + ArabicContent reviewed
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In this course

Study a synthetic community solar cooperative through eighteen developed lessons. Follow meter readings, site reference data, inspection history and daily energy products while learning where logical correctness and physical execution meet.

Progress from typed DataFrames and SQL to deterministic revision selection, shuffles, Parquet publication, adaptive plans, persistence, Python workers, event-time state and recoverable delivery. Every lesson includes explained examples, contextual mistakes, worked exercises and official primary references, with three independent troubleshooting guides.

What you will learn

  • Build typed, null-aware and deterministic reading transformations with explicit source and output grain.
  • Evaluate joins, windows, shuffles, file layout, plans, persistence and UDF boundaries using scoped evidence.
  • Design late-data, checkpoint, sink, security, quality and release contracts without overstating engine or managed-service guarantees.

Your learning path

Three levels

Basic: typed relational foundations

Locate execution, preserve measurement meaning and build correct query populations and totals.

  1. Driver, executors and lazy plans
  2. Schemas, types and source boundaries
  3. Column expressions, NULL and ANSI behavior
  4. Filtering, time zones and deterministic order
  5. Joins, grain and reference data
  6. Aggregations and completeness metrics
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Intermediate: distribution and execution evidence

Choose deterministic revisions and evaluate windows, layout, plans, reuse and worker boundaries.

  1. Windows and deterministic deduplication
  2. Partitions, shuffles and balanced work
  3. Parquet layout and safe publication
  4. Execution plans, statistics and adaptive execution
  5. Persistence, lineage and retry boundaries
  6. Python UDFs, Arrow and worker contracts
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Advanced: streaming and recoverable operation

Connect event-time state, sink outcomes, failure diagnosis, quality, security and release compatibility.

  1. Streaming event time and watermarks
  2. Checkpoints, sinks and delivery semantics
  3. Skew, memory and failure diagnosis
  4. Quality tests and operational evidence
  5. Deployment security and reproducible packaging
  6. Versions, catalogs and table-format boundaries
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Who this is for

  • Developers and data engineers learning PySpark and Spark SQL with explicit correctness and recovery contracts.
  • Engineers reviewing batch and streaming jobs, distributed performance and runtime evidence.

Before you start

  • Basic Python, tabular data and SQL familiarity; all Spark examples are explanatory fragments rather than environment setup commands.
  • Optional engine practice requires a separately authorized disposable Spark environment with compatible runtimes, storage and dependencies.

Related technical solutions

Diagnose invalid casts and missing measurementsSeparate source parsing, selected-version ANSI behavior and domain rejection before replacing failed values or relaxing safety settings.Diagnose slow stages, shuffle and memory pressureUse task distributions and data-grain evidence to distinguish a long tail from a general capacity shortage.Diagnose late stream results and replayed batchesSeparate event-time finality, checkpoint recovery and sink outcomes before changing a streaming query or replaying data.