Apache Spark and Scala Certification Training Course Syllabus

Overview: This comprehensive course is designed to take you from the fundamentals to advanced applications of Apache Spark with Scala, enabling you to build scalable, production-grade data pipelines. The curriculum spans 9 modules, each requiring approximately 6–8 hours of engagement, including lectures and hands-on labs. With a total time commitment of around 60–70 hours, learners gain practical experience in RDDs, DataFrames, Datasets, ETL processing, machine learning with MLlib, performance optimization, and deployment across cluster environments. The course concludes with a capstone project integrating all concepts, ensuring readiness for real-world data engineering challenges.

Module 1: Introduction to Spark & Scala Setup

Estimated time: 7 hours

• Spark ecosystem and architecture
• Driver vs. executor roles
• Setting up Scala IDE or IntelliJ with sbt
• Launching the Spark shell and running first RDD operations

Module 2: RDDs & Core Transformations

Estimated time: 7 hours

• RDD creation methods
• Core transformations: map, filter, flatMap
• Actions: collect, count, reduce
• Building a word-count pipeline using RDDs
• Log analysis using RDD APIs

Module 3: DataFrames & Spark SQL

Estimated time: 7 hours

• DataFrame vs. RDD
• Schema inference and structured data handling
• SparkSession initialization
• Running SQL queries on DataFrames via temp views
• Loading JSON and CSV files into DataFrames

Module 4: Dataset API & Typed Transformations

Estimated time: 7 hours

• Strongly-typed Datasets in Spark
• Using encoders for type safety
• Mapping Datasets to case classes
• Converting DataFrames to Datasets
• Performing type-safe transformations

Module 5: ETL & Data Processing Patterns

Estimated time: 7 hours

• ETL operations: ingestion, transformation, cleansing
• Joining and enriching datasets
• Working with complex types: arrays and maps
• Implementing UDFs in Scala
• Computing moving averages using window functions

Module 6: Machine Learning with MLlib

Estimated time: 7 hours

• Introduction to MLlib and machine learning pipelines
• Feature transformers and data preparation
• Building classification models (e.g., Logistic Regression)
• Implementing clustering algorithms
• Evaluating model performance

Module 7: Performance Tuning & Optimization

Estimated time: 7 hours

• Partitioning strategies for large datasets
• Using broadcast variables and accumulators
• Caching and persistence levels
• Shuffle avoidance techniques
• Resource configuration and tuning via Spark UI

Module 8: Deployment & Cloud Integration

Estimated time: 7 hours

• Using spark-submit for job submission
• Deploying on YARN vs. standalone clusters
• Working with Databricks notebooks
• Integrating with HDFS and S3 storage
• Monitoring Spark applications via Spark UI

Module 9: Capstone Project & Best Practices

Estimated time: 8 hours

• Designing an end-to-end data pipeline
• Code modularization and error handling
• Logging and pipeline monitoring
• Ingesting raw logs and transforming data
• Persisting analytical results to storage

Prerequisites

• Familiarity with Scala programming language
• Basic understanding of big data concepts
• Experience with IDE setup (e.g., IntelliJ) and build tools (e.g., sbt)

What You'll Be Able to Do After

• Build and optimize scalable data processing pipelines using Spark and Scala
• Apply core RDD and high-level DataFrame/Dataset APIs to real-world datasets
• Design and execute ETL workflows with complex transformations and window functions
• Deploy Spark applications across cluster managers like YARN and Databricks
• Use Spark UI and tuning techniques to diagnose and improve job performance