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Table of Contents
Table of Contents
Apache Spark has transformed distributed data processing by enabling in-memory computation at scale, becoming the de facto framework for big data analytics across industries.
What You Will Learn
- 1 Understand the evolution and architecture of Apache Spark.
- 2 Learn Spark's core data abstractions: RDDs, DataFrames, and Datasets.
- 3 Apply transformations and actions on distributed data.
- 4 Implement Spark SQL for structured data queries.
- 5 Build streaming pipelines with Spark Structured Streaming.
- 6 Optimize Spark performance through tuning and partitioning.
- 7 Implement machine learning workflows with Spark MLlib.
- 8 Manage Spark cluster deployment and configuration.
- 9 Develop end-to-end data pipelines integrating Spark with Hadoop ecosystem.
- 10 Apply Spark for real-time analytics and batch processing use cases.
What's Included
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Spark Processing Framework