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Spark Production Engineering & Troubleshooting

Own Spark jobs end-to-end — sizing, optimization, debugging, and interview-grade reasoning

Course Summary

RADE™ Apache Spark Production Engineering & Troubleshooting is an advanced, capstone-level course designed to teach you how to own Spark workloads in real production environments.

This is the stage where Spark stops being a development tool and becomes a business-critical system — governed by SLAs, cost constraints, and reliability expectations.

In this course, you will learn how senior data engineers design, size, optimize, and debug Spark pipelines systematically — without guesswork.

What you will learn:

 Cluster Sizing & Resource Engineering

  • How to answer: “How do you process 100GB+ of data?”

  • SLA-driven sizing approach

  • Cost-optimized cluster selection

  • Resource reservation rules:

    • OS & YARN

    • Application Master

    • Driver

  • Optimal executor design:

    • Thin vs fat vs optimal executors

    • AWS-recommended 4–5 cores per executor

  • Memory overhead & off-heap calculations

  • Why partition size matters more than total data size

 Production-Grade Performance Optimization

  • 14+ real-world optimization techniques

  • Data formats:

    • Why Parquet is default

    • Compression & predicate pushdown

  • Partitioning strategies for downstream consumers

  • Caching & persistence (used correctly)

  • Wide transformation minimization

  • Broadcast joins, AQE, and salting

  • Shuffle partition tuning (1–200 MB rule)

  • Bucketing for reusable datasets

  • UDF performance hierarchy and trade-offs

 Spark UI Mastery & Troubleshooting

  • Systematic debugging methodology

  • Reading Spark UI like a pro:

    • Jobs tab

    • Stages tab

    • SQL tab

    • Executors tab

    • Storage tab

  • Identifying:

    • data skew

    • shuffle bottlenecks

    • memory pressure

    • GC issues

    • disk spilling

  • Fixing slow jobs step-by-step

  • Preventing regressions after code or data changes

 Interview & Real-World Scenarios

  • Structured answers to senior-level Spark questions

  • Explaining trade-offs clearly (performance vs cost)

  • Communicating Spark decisions with confidence

  • Thinking like a lead / owner, not an implementer

 Outcome

By the end of this course, you will be able to:

  • Design Spark clusters from first principles

  • Meet SLAs with minimum cost

  • Diagnose and fix slow Spark jobs confidently

  • Explain Spark decisions clearly in interviews

  • Take full ownership of Spark pipelines in production

This course is Week 5 of the RADE™ Apache Spark series, and serves as the capstone that completes your Spark mastery journey.

Course Curriculum

Sachin Chandrashekhar

Lead Data Engineer @ World's #1 Airline

Course Pricing

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