Live Online Apache Spark Course for Data Engineering

Live Course Module: Apache Spark Course for Data Engineering

Total Duration: 40 Hours (5 Weeks)

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Week 1: Introduction to Apache Spark and Big Data Fundamentals

Total Time: 8 hours

1. Introduction to Big Data Ecosystem (1 hr)

   • What is Big Data?

   • Data Engineering vs Data Science

   • Role of Spark in Modern Data Architecture

2. Overview of Apache Spark (1 hr)

   • Spark’s core concepts

   • Spark vs Hadoop MapReduce

   • Spark Components and Architecture

3. Spark Cluster Overview (1.5 hrs)

   • Spark driver, executors, cluster managers

   • Standalone, YARN, and Mesos modes

4. Spark Installation and Setup (2 hrs – Lab)

   • Local setup using PySpark / Databricks

   • Running first Spark job

5. Hands-On & Assignment (2.5 hrs)

   • Word Count example

   • Explore Spark UI

   • Assignment: Build and run a Spark application locally

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Week 2: Spark Core and RDD Programming

Total Time: 8 hours

1. Understanding RDDs (1 hr)

   • What are RDDs?

   • Lazy evaluation & DAGs

2. Transformations and Actions (2 hrs)

   • Map, Filter, ReduceByKey, FlatMap, Join

   • Common actions: Collect, Count, Take

3. Persistence and Caching (1 hr)

   • Memory management and optimization

4. Working with Key-Value Pairs (1.5 hrs)

   • Pair RDDs and aggregations

5. Hands-On & Assignment (2.5 hrs)

   • RDD transformations practice

   • Assignment: Build a data processing pipeline using RDDs

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Week 3: Spark SQL and DataFrames for Data Engineering

Total Time: 8 hours

1. Introduction to Spark SQL (1 hr)

   • Structured APIs overview

   • Catalyst optimizer and Tungsten engine

2. DataFrame Operations (2 hrs)

   • Creating DataFrames from JSON, CSV, Parquet

   • Schema inference and transformations

3. Spark SQL Queries (1.5 hrs)

   • Registering temporary views

   • Writing SQL queries in Spark

4. Data Sources and Connectors (1.5 hrs)

   • Working with JDBC, S3, Delta Lake

5. Hands-On & Assignment (2 hrs)

   • Build ETL job using DataFrames

   • Assignment: Transform and load structured data

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Week 4: Advanced Spark Concepts and Optimization

Total Time: 8 hours

1. Spark Streaming (1.5 hrs)

   • Introduction to Structured Streaming

   • Working with real-time data sources

2. Performance Tuning and Optimization (2 hrs)

   • Partitioning, Caching, and Shuffle operations

   • Broadcast variables and accumulators

3. Spark Joins and Aggregations (1.5 hrs)

   • Efficient join strategies

   • Window and group operations

4. Monitoring and Debugging (1 hr)

   • Spark UI metrics and troubleshooting

5. Hands-On & Assignment (2 hrs)

   • Streaming job with Kafka data source

   • Assignment: Optimize a Spark ETL job

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Week 5: Integrations, Workflows, and Project

Total Time: 8 hours

1. Integration with Data Engineering Tools (1.5 hrs)

   • Spark with Airflow, Kafka, and Delta Lake

   • Spark on Databricks and AWS EMR

2. Deployment and Productionization (1.5 hrs)

   • Packaging Spark applications

   • Job scheduling and CI/CD pipelines

3. Capstone Project (3 hrs)

   • End-to-end ETL pipeline

   • Load raw data → Clean → Transform → Store in Data Lake/Warehouse

4. Final Review and Q&A (2 hrs)

   • Project presentation

   • Certification guidance and career tips

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🧩 Final Deliverables

• Mini Projects: 3 (RDD, SQL, Streaming)

• Capstone Project: 1 End-to-End Data Engineering Pipeline

• Assessments: Weekly quizzes + project evaluation