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.