Description

Description

This course provides an in-depth introduction to Apache Spark 3 for distributed computing. Designed for developers, data analysts, and architects, it focuses on leveraging Spark’s powerful engine for big data processing using Python (PySpark). The course covers core Spark concepts, Resilient Distributed Datasets (RDDs), DataFrames, Spark SQL, and Structured Streaming for real-time data processing.

Through hands-on exercises, participants will learn how to interact with Spark efficiently, optimize queries, and integrate with Kafka for streaming data ingestion.

Training Objectives

• Participants will:
• Understand Apache Spark’s architecture and its advantages over traditional big data frameworks.
• Work with RDDs transformations and actions for distributed computations.
• Utilize Spark SQL and the DataFrame API for structured data processing.
• Leverage Spark’s Catalyst optimizer and Tungsten engine for query performance.
• Process real-time streaming data with Spark Structured Streaming.
• Integrate Kafka with Spark Streaming for event-driven data ingestion.
• Optimize Spark applications using caching shuffling strategies and broadcast variables.
• Develop standalone Spark applications using PySpark.

Course Outline

• Session 1: Introduction to Spark<
• Overview of Apache Spark and its role in distributed computing<
• Comparing Spark vs. Hadoop for big data processing<
• Spark’s ecosystem and core components<
• Setting up Spark and PySpark environment<
• Lab: Running Spark in local and cluster mode<
• Session 2: Understanding RDDs and Spark Architecture<
• RDD concepts, transformations, and lazy evaluation<
• Data partitioning, pipelining, and fault tolerance<
• Applying map(), filter(), reduce(), and other RDD operations<
• Lab: Creating and manipulating RDDs<
• Session 3: Working with DataFrames and Spark SQL<
• Introduction to Spark SQL and DataFrames<
• Creating and querying DataFrames using SQL-based and API-based approaches<
• Working with different data formats (JSON, CSV, Parquet, etc.)<
• Lab: Querying structured data using Spark SQL<
• Session 4: Performance Optimization in Spark<
• Understanding shuffling and data locality<
• Catalyst query optimizer (explain() and query execution plans)<
• Tungsten optimizations (binary format, whole-stage code generation)<
• Lab: Optimizing Spark queries for performance<
• Session 5: Spark Structured Streaming<
• Introduction to stream processing and event-driven architecture<
• Working with Structured Streaming API<
• Processing real-time data in a continuous query model<
• Lab: Building a streaming data pipeline in Spark<
• Session 6: Integrating Spark with Kafka<
• Overview of Kafka and event-driven data streaming<
• Using Spark to consume and process Kafka streams<
• Configuring Kafka as a data source and sink<
• Lab: Ingesting and processing real-time Kafka data with Spark<
• Session 7: Advanced Performance Tuning<
• Caching and data persistence strategies<
• Reducing shuffling for efficient computation<
• Using broadcast variables and accumulators<
• Lab: Implementing caching and shuffling optimizations<
• Session 8: Building Standalone Spark Applications<
• Creating Spark applications using PySpark API<
• Configuring SparkSession and application parameters<
• Running Spark applications on local and cluster environments<
• Lab: Developing a PySpark application and submitting jobs<
• By the end of this course, participants will be able to develop scalable data processing applications using Apache Spark 3 and Python (PySpark) effectively.