A Crash Course In PySpark

Module 1: Getting Started with Spark & PySpark

⏳ 30 minutes

• Installing Spark, setting up pyspark interactive shell and Jupyter integration

• Overview of Spark architecture: driver, executors, and cluster modes

Module 2: RDDs & Core Transformations

⏳ 45 minutes

• Creating RDDs from files and in-memory collections

• Applying transformations (map, filter, flatMap, reduceByKey) and actions (collect, count, take)

Module 3: DataFrames & Spark SQL

⏳ 1 hour

• Creating Spark DataFrames from CSV, JSON, and Parquet files

• Using DataFrame operations (select, filter, groupBy, join) and running SQL queries

Module 4: Performance Tuning & Optimizations

⏳ 45 minutes

• Understanding the Catalyst optimizer and Tungsten engine

• Repartitioning, caching, and using broadcast joins for large tables

Module 5: Advanced Data Processing

⏳ 1 hour

• Working with window functions, UDFs, and complex types (arrays, structs)

• Handling skew and writing efficient data pipelines

Module 6: Spark Streaming Essentials

⏳ 45 minutes

• Processing real-time data with Structured Streaming

• Applying streaming transformations and writing output to sinks

Module 7: Machine Learning with MLlib

⏳ 1 hour

• Building ML pipelines: data preprocessing, feature engineering, and model training

• Evaluating models and tuning hyperparameters for classification and regression

Module 8: Putting It All Together

⏳ 30 minutes

• End-to-end ETL pipeline example: ingest, transform, analyze, and persist results

• Best practices for debugging, logging, and monitoring Spark applications

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