This 30-day intensive program is designed to help you master the core tools used in modern data engineering: Apache Spark for large-scale data processing, Apache Kafka for real-time data streaming, and Apache Airflow for workflow orchestration.

You will begin by understanding the fundamentals of batch and real-time data processing, followed by hands-on learning with Apache Spark to process and transform large datasets efficiently. The course then dives into Apache Kafka, where you will learn how to build real-time data pipelines using producers, consumers, and event-driven architectures.

Next, you will learn Apache Airflow to schedule, automate, and monitor data workflows. You will also integrate Spark and Kafka with Airflow to build end-to-end, production-ready pipelines.

Through real-world projects, you will gain practical experience in designing scalable systems, handling failures, and optimizing performance.

By the end of this course, you will be able to build robust, scalable, and real-time data pipelines used in modern data platforms.

Week 1: Foundations + Spark

• Batch vs Real-time processing
• Intro to Spark (RDD, DataFrames)
• Transformations & Actions
• Spark SQL

Week 2: Spark Advanced

• Performance optimization
• Partitioning
• Handling big data
• Mini project

Week 3: Kafka (Real-Time)

• Producers & Consumers
• Topics, partitions
• Streaming pipelines
• Kafka + Spark Streaming

Week 4: Airflow + Integration

• DAGs, scheduling
• Monitoring pipelines
• Spark + Kafka + Airflow integration
• Final End-to-End Projec