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Big Data with Spark and Hadoop
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This book teaches big data systems in simple language from beginner to practical platform design. It covers Hadoop, Spark, distributed processing, data lakes, tuning, monitoring, and real-world data pipeline patterns.
Level: intermediateLanguage: EN EnglishRating: 0Reviews: 0
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Book contents
01Chapter 1: Introduction to Big Data Systems02Chapter 2: Batch Processing and Distributed Storage03Chapter 3: Hadoop Foundations and HDFS04Chapter 4: MapReduce and Cluster Thinking05Chapter 5: Spark Fundamentals and Execution Model06Chapter 6: PySpark for Practical Data Processing07Chapter 7: Data Lakes, File Formats, and Partitioning08Chapter 8: Resource Management and Scheduling09Chapter 9: ETL Pipelines at Scale10Chapter 10: Performance Tuning for Spark Jobs11Chapter 11: Monitoring and Debugging Big Data Workloads12Chapter 12: Security and Governance in Data Platforms13Chapter 13: Streaming and Near-Real-Time Extensions14Chapter 14: Cost Optimization in Data Processing15Chapter 15: Capstone: Build a Spark Data Pipeline16Chapter 16: Capstone: Operate a Hadoop and Spark Stack17Appendix A: Commands and Workflow Cheat Sheet18Appendix B: Reference Architectures and Patterns19Appendix C: Common Mistakes and Fixes20Appendix D: Best Practices Summary
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