Data engineers, data analysts, developers, and IT professionals interested in big data processing and analytics

Basic knowledge of Python or Scala, SQL, and fundamental data processing concepts; familiarity with distributed systems is a plus

Course Outline:

Module 1: Introduction to Big Data

1. What is big data (Volume, Velocity, Variety)
2. Traditional vs distributed data processing
3. Overview of big data ecosystem

Module 2: Introduction to Apache Spark

1. Spark architecture and components
2. Spark vs Hadoop MapReduce
3. Setting up Spark environment

Module 3: Working with Spark Core and RDDs

1. Resilient Distributed Datasets (RDDs)
2. Transformations and actions
3. Fault tolerance and parallel processing

Module 4: Spark DataFrames and Spark SQL

1. DataFrames and datasets
2. Querying data using Spark SQL
3. Data manipulation and aggregation

Module 5: Data Processing and ETL Pipelines

1. Data ingestion and transformation
2. Building ETL workflows
3. Handling large datasets efficiently

Module 6: Introduction to Spark MLlib

1. Overview of machine learning in Spark
2. Basic ML workflows
3. Example use cases

Module 7: Performance Optimization and Best Practices

1. Caching and partitioning
2. Performance tuning techniques
3. Monitoring and debugging Spark jobs