Apache Flink
Introduction to Apache Flink
Apache Flink is an open-source stream processing framework designed for high-throughput, low-latency data processing. This module introduces Apache Flink, covering its architecture, core features, and use cases in real-time data processing.
Setting Up Apache Flink
Learn how to install and configure Apache Flink. This section covers system requirements, installation procedures, and initial setup. Explore how to configure Flink clusters, including job managers, task managers, and other essential components.
Understanding Flink’s Architecture
Discover Apache Flink’s architecture, including its key components and data flow mechanisms. Learn about the role of job managers, task managers, and the distributed runtime. Explore how Flink’s architecture supports scalable and resilient stream processing.
Developing Flink Applications
Gain insights into developing applications with Apache Flink. Learn about the Flink API, including DataStream and Table APIs. Explore how to build and deploy Flink jobs, handle data sources and sinks, and manage stateful computations.
Managing Flink Jobs
Understand how to manage and monitor Flink jobs. Learn about job submission, execution, and management using the Flink dashboard. Explore how to handle job failures, optimize job performance, and scale Flink applications.
Advanced Features and Optimizations
Explore advanced features of Apache Flink, such as windowing, event time processing, and complex event processing. Learn about performance optimizations, including resource management and tuning parameters to enhance job efficiency and throughput.
Integration with Other Systems
Discover how to integrate Apache Flink with other systems and technologies. Learn about connectors for databases, message queues, and cloud platforms. Explore how to use Flink with various data sources and sinks to build end-to-end data processing pipelines.
Security and Best Practices
Learn about security considerations and best practices for using Apache Flink. Explore how to configure security settings, manage access control, and ensure data protection. Understand best practices for developing, deploying, and maintaining Flink applications.
Troubleshooting and Maintenance
Gain insights into troubleshooting and maintaining Apache Flink clusters. Learn how to diagnose and resolve common issues, perform regular maintenance tasks, and ensure the reliability and stability of your Flink environment.
Apache Flink Syllabus
1. Introduction to Apache Flink
- Overview of Apache Flink and its evolution
- Comparison with other stream processing frameworks
- Apache Spark
- Apache Storm
- Use cases and scenarios suitable for Apache Flink
2. Apache Flink Architecture
- Understanding Flink's architecture
- JobManager
- TaskManager
- JobGraph
- Execution model and data flow in Flink
- Fault tolerance and checkpointing mechanisms
3. Flink Data Streaming Basics
- Introduction to data streams and data transformations
- Windowing concepts
- Time-based
- Count-based
- Event time processing and watermarks
4. Flink APIs
- Overview of Flink APIs
- DataStream API
- DataSet API
- Writing and deploying Flink applications
- Key transformations
- map
- flatMap
- filter
- reduce
- etc.
5. Stateful Stream Processing
- Introduction to stateful computations in Flink
- Managing state with KeyedState and OperatorState
- State backend configurations and tuning
6. Flink Connectors
- Working with Flink connectors
- Kafka
- Apache Cassandra
- Elasticsearch
- etc.
- Customizing connectors and handling data sources and sinks
- Using Table API and SQL for data integration
7. Advanced Flink Topics
- Exactly-once processing semantics
- Dynamic scaling and resource management
- Handling late data and out-of-order events
- Flink’s integration with Apache Beam
8. Monitoring and Operations
- Monitoring Flink applications
- Web UI
- Metrics
- Logging and debugging Flink jobs
- Configuration management and best practices
9. Performance Optimization
- Tuning Flink applications for better performance
- Memory management and JVM options
- Optimizing parallelism and throughput
10. Real-time Use Cases and Case Studies
- Reviewing real-world applications of Apache Flink
- Case studies from various industries
- Finance
- Telecommunications
- Lessons learned and best practices from deployments
11. Flink Ecosystem and Extensions
- Overview of Flink's ecosystem
- FlinkML
- FlinkCEP
- etc.
- Exploring Flink extensions and community contributions
- Integrating Flink with Apache Hadoop and other data processing frameworks
Training
Basic Level Training
Duration : 1 Month
Advance Level Training
Duration : 1 Month
Project Level Training
Duration : 1 Month
Total Training Period
Duration : 3 Months
Course Mode :
Available Online / Offline
Course Fees :
Please contact the office for details