Live Online Apache Flink Course for Data Analytics

Live Course Module: Apache Flink Course for Data Analytics

Total Duration: 40 Hours (4 Weeks)

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Week 1: Introduction to Apache Flink and Core Concepts (6 Hours)

1. Overview of Apache Flink and its role in modern data analytics

2. Understanding distributed stream and batch processing

3. Flink architecture – Job Manager, Task Manager, and DataFlow

4. Setting up Apache Flink (Local/Cluster mode)

5. Writing your first Flink application

6. Hands-on: Data stream basics and running simple jobs

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Week 2: DataStream API and Transformations (6 Hours)

1. Working with Flink’s DataStream API

2. Key transformations: map, flatMap, filter, reduce, and aggregate

3. Handling event time and processing time

4. Understanding windows (tumbling, sliding, session windows)

5. State management and checkpointing fundamentals

6. Hands-on: Real-time stream transformations and aggregation exercises

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Week 3: Advanced Stream Processing and Integrations (6 Hours)

1. Connecting Flink with Kafka for real-time data ingestion

2. Integrating with external systems (HDFS, Cassandra, JDBC, Elasticsearch)

3. Flink Table API and SQL for declarative analytics

4. Working with stateful streaming and process functions

5. Managing late data and watermarks

6. Hands-on: Building a streaming pipeline with Kafka + Flink + HDFS

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Week 4: Flink in Production and Analytics Project (6 Hours)

1. Flink cluster deployment and scaling strategies

2. Monitoring, metrics, and performance optimization

3. Error handling, fault tolerance, and backpressure management

4. Advanced use cases – IoT analytics, real-time dashboards, anomaly detection

5. Capstone Project: End-to-End Real-Time Analytics Pipeline using Flink

6. Final review, assessment, and Q&A

🧩 Mini Project Ideas (Week 4 Hands-on)

Learners will design and deploy real-time data analytics applications such as:

1. Project 1: Real-Time Log Monitoring System using Flink + Kafka

2. Project 2: Sensor Data Stream Analytics with Flink SQL

3. Project 3: Fraud Detection Pipeline using Flink CEP and ML Integration

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🧑‍🏫 Teaching Methodology

• Live Interactive Sessions with practical demos

• Hands-on Labs after each topic

• Assignments & Quizzes for concept reinforcement

• Mini Project & Peer Review during final week

• Q&A and Debugging Sessions for practical problem-solving

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🏁 Final Deliverables

• Certificate of Completion

• End-to-End Streaming Analytics Project

• Strong understanding of Flink for real-time and batch data analytics

Course Outcomes:

By the end of this course, learners will be able to:

• Understand Apache Flink’s architecture, APIs, and ecosystem.

• Develop Flink applications for both batch and real-time stream processing.

• Integrate Flink with data sources like Kafka, Hadoop, and databases.

• Implement analytics and transformations using Flink DataStream and Table APIs.

• Apply Flink for use cases in data analytics and predictive processing.