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Spark: Syllabus
Mastering Apache Spark: Scalable Distributed Data Processing with Python
Apache Spark is a powerful open-source engine for large-scale distributed data processing. This book provides a hands-on approach to mastering Spark, covering batch processing, real-time streaming, performance optimizations, and integrations with modern data lake architectures using Delta Lake and MinIO.
Module 1: Introduction to Apache Spark 3.5.5
- Evolution of Apache Spark and key features in version 3.5.5
- Understanding Spark’s distributed architecture (RDDs, DAGs, Executors, and Tasks)
- Setting up Apache Spark with Python (PySpark)
- Running Spark in local mode vs. cluster mode
Module 2: Spark Core and RDDs (Resilient Distributed Datasets)
- Understanding the fundamentals of RDDs
- RDD transformations and actions
- Optimizing RDD operations for performance
- Working with RDD persistence and caching
Module 3: DataFrames and Spark SQL
- Introduction to DataFrames and their advantages over RDDs
- Querying structured data using Spark SQL
- Working with Parquet, JSON, and Avro file formats
- Optimizing DataFrame operations with Catalyst Optimizer
Module 4: Spark Performance Optimization and Tuning
- Partitioning strategies for large-scale data processing
- Configuring Spark memory management and shuffle optimization
- Using Broadcast variables and Accumulators for efficiency
- Monitoring Spark jobs with Spark UI and structured logging
Module 5: Streaming Data Processing with Spark Structured Streaming
- Introduction to real-time streaming and event-driven architecture
- Processing data streams from Kafka and MinIO
- Stateful aggregations and windowed operations
- Fault tolerance and checkpointing in Spark Streaming
Module 6: Machine Learning with Spark MLlib
- Overview of Spark MLlib and its machine learning pipeline
- Feature engineering and transformations in Spark MLlib
- Training and evaluating models with distributed ML algorithms
- Hyperparameter tuning and cross-validation in Spark
Module 7: Graph Processing with GraphX
- Introduction to GraphX and graph-based computations
- Building and analyzing social network graphs
- PageRank and community detection algorithms
- Optimizing large-scale graph processing workloads
Module 8: Integrating Apache Spark with Delta Lake
- Understanding Delta Lake’s ACID transactions and schema evolution
- Using Delta Lake for scalable batch and streaming ingestion
- Time travel and versioning in Delta tables
- Optimizing Delta Lake performance with compaction and indexing
Module 9: Object Storage Integration with MinIO
- Understanding MinIO as an S3-compatible storage solution
- Reading and writing Spark DataFrames to MinIO
- Implementing data lake solutions with Spark and MinIO
- Configuring security and access control for MinIO storage
Module 10: Deploying Apache Spark in Production
- Running Spark on Kubernetes, AWS EMR, and Databricks
- Automating Spark job workflows with Apache Airflow
- Implementing CI/CD pipelines for Spark applications
- Monitoring, scaling, and troubleshooting production Spark jobs
Hands-On Projects
Project 1: Batch Data Processing Pipeline with Spark and Delta Lake
- Load large-scale datasets into Delta Lake
- Perform batch transformations and aggregations with Spark
- Implement time travel and schema enforcement
Project 2: Real-Time Streaming with Kafka, Spark, and MinIO
- Stream data from Kafka into Spark Structured Streaming
- Store streaming data in MinIO as an object store
- Process and visualize real-time analytics
Project 3: Machine Learning Pipeline with Spark MLlib
- Train a large-scale ML model on distributed data
- Perform feature engineering using Spark transformations
- Deploy the trained model for batch inference
Project 4: Building a Scalable ETL Pipeline with Apache Spark
- Extract data from multiple sources and transform using PySpark
- Load processed data into Delta Lake and MinIO
- Automate ETL workflow with Apache Airflow
Project 5: Deploying and Monitoring Apache Spark on Kubernetes
- Set up a Spark cluster on Kubernetes
- Run distributed Spark jobs with dynamic resource allocation
- Monitor job execution with Prometheus and Grafana