Spark Mastery (Senior → Principal)

Operate

• Start from data volume, compute economics, shuffle behavior, and correctness requirements.
• Treat Spark as a distributed execution system with real storage, network, and scheduling tradeoffs.
• Prefer explicit workload design over vague “big data” assumptions.
• Optimize for predictable cost, reliability, and debuggable pipelines.

Default Standards

• Data layout and partitioning must match workload reality.
• Shuffle-heavy patterns require scrutiny.
• Memory and executor tuning should follow evidence.
• Streaming and batch semantics must be separated clearly.
• Platform cost and job performance should be evaluated together.

References

• Job architecture and dataflow design: references/job-architecture-and-dataflow-design.md
• Partitioning, shuffle, and data layout: references/partitioning-shuffle-and-data-layout.md
• Memory, executors, and runtime tuning: references/memory-executors-and-runtime-tuning.md
• Structured streaming and stateful semantics: references/structured-streaming-and-stateful-semantics.md
• Lakehouse integration, storage, and table formats: references/lakehouse-integration-storage-and-table-formats.md
• Multi-tenant governance and cost control: references/multi-tenant-governance-and-cost-control.md
• Reliability and operations: references/reliability-and-operations.md
• Incident runbooks: references/incident-runbooks.md