Live Online Apache Flink Course for Data Engineering

Live Course Module: Apache Flink Course for Data Engineering

Total Duration: 40 Hours (5 Weeks)

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Week 1: Introduction to Real-Time Data Processing and Apache Flink

Total Time: 8 hours

1. Introduction to Stream Processing (1 hr)

   • Batch vs Stream Processing

   • Use cases of stream processing in Data Engineering

2. Overview of Apache Flink (1.5 hrs)

   • What is Apache Flink?

   • Core features and architecture

   • Comparison with Spark Streaming and Kafka Streams

3. Flink Ecosystem and Components (1.5 hrs)

   • Flink Runtime, APIs, and Connectors

   • Job Manager and Task Manager

4. Setting up Flink Environment (2 hrs – Lab)

   • Installing and configuring Flink locally or on cloud (AWS/GCP)

   • Running sample streaming and batch jobs

5. Hands-On & Assignment (2 hrs)

   • Simple WordCount example in Flink

   • Assignment: Run and monitor a Flink job using the web UI

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Week 2: Flink Programming Model – DataStream API and DataSet API

Total Time: 8 hours

1. Understanding Flink Data Model (1 hr)

   • DataSet API vs DataStream API

   • Execution model and transformations

2. Flink DataStream API (2 hrs)

   • Basic operations: map, filter, keyBy, reduce

   • Windows and triggers

3. Flink DataSet API for Batch Processing (1.5 hrs)

   • Batch transformations and aggregations

   • Integrating batch with streaming pipelines

4. Stateful Stream Processing (1.5 hrs)

   • Operator state and keyed state

   • State backends and fault tolerance

5. Hands-On & Assignment (2 hrs)

   • Build a data transformation pipeline using DataStream API

   • Assignment: Create a real-time data aggregation job

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Week 3: Time, Windows, and Event Processing in Flink

Total Time: 8 hours

1. Event Time vs Processing Time (1.5 hrs)

   • Understanding time semantics

   • Watermarks and lateness handling

2. Windowing Concepts (2 hrs)

   • Tumbling, Sliding, and Session windows

   • Aggregations and custom windows

3. Event Processing Patterns (1.5 hrs)

   • Handling out-of-order and late data

   • CEP (Complex Event Processing) overview

4. Hands-On & Assignment (3 hrs)

   • Implement time-based window aggregations

   • Assignment: Detect specific event patterns using Flink CEP

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Week 4: Flink Connectors, State Management, and Checkpointing

Total Time: 8 hours

1. Integrating Flink with Data Sources and Sinks (2 hrs)

   • Kafka, Kinesis, and FileSystem connectors

   • Writing to databases and message queues

2. State Management in Flink (1.5 hrs)

   • State storage and recovery

   • RocksDB state backend

3. Fault Tolerance and Checkpointing (1.5 hrs)

   • Checkpointing and savepoints

   • Restart strategies and job recovery

4. Hands-On & Assignment (3 hrs)

   • Build a streaming pipeline from Kafka → Flink → S3

   • Assignment: Implement checkpointing and verify fault recovery

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Week 5: Advanced Topics, Deployment, and Capstone Project

Total Time: 8 hours

1. Flink SQL and Table API (2 hrs)

   • Writing SQL queries on streams

   • Integrating Flink SQL with Kafka and Hive

2. Performance Optimization and Monitoring (1.5 hrs)

   • Parallelism and task slot tuning

   • Metrics and dashboards

3. Deployment and Integration (1.5 hrs)

   • Running Flink on Kubernetes, YARN, and AWS EMR

   • CI/CD and production best practices

4. Capstone Project (3 hrs)

   • End-to-end real-time data pipeline

   • Kafka → Flink → PostgreSQL or Data Lake

   • Data cleaning, transformation, and aggregation

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🧩 Final Deliverables

• Mini Projects: 3 (DataStream API, Window Processing, Kafka Integration)

• Capstone Project: 1 Real-Time Data Engineering Pipeline

• Assessments: Weekly quizzes + project review

• Tools Covered: Apache Flink, Kafka, S3, Hive, RocksDB, Kubernetes