[00] – Apache Spark By Example Course

This tutorial series will guide you from the fundamentals of Apache Spark to advanced concepts, with hands-on examples to help you become proficient in big data processing. Each section will build on the previous one, using Scala in AWS Glue (or local Spark setups).

Part 1: Introduction to Apache Spark #

What is Apache Spark? #

• Overview of Spark’s architecture
• Differences between Spark and Hadoop MapReduce
• Use cases in real-world applications

Setting Up Spark #

• Installing Spark locally
• Running Spark on Jupyter
• Introduction to Spark Shell and Spark Submit

First Spark Application #

• Writing and running a “Hello World” Spark job
• Understanding RDDs, DataFrames, and Datasets

Part 2: Working with Data in Spark #

Spark Core Concepts #

• Resilient Distributed Datasets (RDDs) vs. DataFrames vs. Datasets
• Understanding transformations and actions
• Lazy evaluation and DAG (Directed Acyclic Graph)

Data Ingestion in Spark #

• Reading data from CSV, JSON, Parquet, and Databases
• Loading data from AWS S3 (Glue Catalog)
• Handling schema inference

Data Transformations #

• Using map(), filter(), flatMap()
• Performing groupBy(), reduceByKey(), aggregateByKey()
• Joining datasets efficiently (join, broadcast join, map-side join)

Writing Data Back #

• Writing to CSV, Parquet, and JSON
• Partitioning and bucketing for optimized queries
• Writing to AWS S3, Glue Catalog, and Databases

Part 3: Advanced Transformations and Optimization #

Spark SQL and DataFrames API #

• Creating and querying DataFrames
• Registering temporary views and using SQL queries
• Performance comparison: RDDs vs. DataFrames vs. Datasets

Optimization Techniques #

• Catalyst Optimizer & Tungsten Execution Engine
• Understanding lazy execution and Spark jobs
• Repartitioning vs. Coalescing for performance
• Using persist() and cache() effectively

Working with UDFs (User-Defined Functions) #

• Writing custom Scala UDFs
• Using Pandas UDFs for performance (Spark 3.0+)

Part 4: Spark Streaming & Real-Time Data Processing #

Introduction to Structured Streaming #

• Difference between batch processing and stream processing
• Understanding the micro-batch model

Building a Real-Time Streaming Pipeline #

• Reading from Kafka, AWS Kinesis
• Writing to S3, Databases, NoSQL stores (DynamoDB, MongoDB)

Stateful Processing #

• Handling watermarking, aggregation, and window functions
• Using checkpointing and stateful operations

By the end of this course, you will have a strong understanding of Apache Spark and its real-world applications, from basic data transformations to advanced optimizations and streaming pipelines.