Apache Flink Training and Certification in Kraków, Poland

Apache Flink training provides comprehensive knowledge on building real-time data streaming applications using the Flink framework. It covers core concepts such as stream processing, stateful computations, event time processing, and fault tolerance. Participants gain hands-on experience with Flink's APIs for Java, Scala, and Python, and learn to design scalable data pipelines that handle high-throughput and low-latency requirements. The training also emphasizes integrating Flink with other big data systems like Kafka, HDFS, and Hadoop for end-to-end data processing. By completing this course, professionals can build robust, real-time analytics applications that process large volumes of data efficiently.

Introduction to Apache Flink and Stream Processing Basics

• Introduction to Apache Flink:

• Overview of Apache Flink and its ecosystem.

• Flink vs other stream processing frameworks (e.g., Apache Kafka, Apache Spark).

• Flink architecture: Job Manager, Task Manager, and components.

• Hands-on Exercise

• Stream Processing Concepts:

• Understanding stream processing and its use cases.

• Introduction to Flink’s DataStream API.

• Understanding time semantics in Flink (Event time, Processing time, and Ingestion time).

• Hands-on Exercise

• Flink Transformations:

• Flink transformations: map(), filter(), flatMap(), and reduce().

• Understanding windowing in Flink (Tumbling, Sliding, Session windows).

• Hands-on Exercise

• Flink's Fault Tolerance and State Management:

• Flink’s stateful stream processing: Managed state, timers, and keying.

• Checkpointing and recovery in Flink.

• Hands-on Exercise

Advanced Flink Features, Batch Processing, and Deployment

• Advanced Flink Features:

• Flink’s CEP (Complex Event Processing) library for pattern detection.

• Flink’s connectors: Integrating with external systems (e.g., Kafka, HDFS, JDBC).

• Hands-on Exercise.

• Flink Batch Processing:

• Introduction to batch processing with Flink’s DataSet API.

• Differences between stream and batch processing in Flink.

• Hands-on Exercise

• Flink’s SQL API:

• Using Flink’s SQL API for querying data streams and batch data.

• Introduction to Flink Table API for complex queries.

• Hands-on Exercise

• Flink Deployment and Scaling:

• Running Flink applications in standalone and cluster mode.

• Deploying Flink jobs on Flink’s cluster or cloud environments.

• Hands-on Exercise