Course Agenda
Introduction to Big Data and Apache Spark
What is Big Data? Challenges and opportunities
Introduction to Apache Spark and its ecosystem
Comparison of Spark with Hadoop MapReduce
Setting up Spark with Python (PySpark)
Spark Architecture and Execution Model
Spark components: Driver, Executors, Cluster Manager
RDDs (Resilient Distributed Datasets) vs. DataFrames vs. Datasets
Spark execution flow and DAG (Directed Acyclic Graph)
Working with Spark RDDs and Transformations
Creating and manipulating RDDs
Lazy evaluation and transformations (map, filter, reduceByKey)
Actions (collect, count, take, foreach)
Spark DataFrames and Spark SQL
Introduction to DataFrames and their advantages
Schema inference and working with structured data
Performing SQL operations using Spark SQL
Reading and writing data from various sources (CSV, JSON, Parquet)
Data Processing with PySpark
Handling missing data and data cleansing techniques
Aggregation and group operations
Joins and window functions in PySpark
Optimizing Spark Performance
Partitioning strategies and shuffling
Caching and persistence
Broadcast variables and accumulator optimization
Tuning Spark jobs for better resource utilization
Machine Learning with Spark MLlib
Introduction to MLlib and Spark ML Pipelines
Building and evaluating machine learning models
Feature engineering with PySpark
Deploying machine learning models in Spark
Real-World Big Data Use Cases
Processing large datasets with Spark
ETL pipelines using PySpark
Streaming data processing with Spark Streaming
Integration with cloud platforms (AWS, Azure, GCP)
Hands-on Project and Capstone Assignment
Developing an end-to-end Big Data processing pipeline
Implementing Spark jobs for large-scale data analytics
Optimizing performance for scalability
