Apache Flink – Stream Processing

Module 1: Introduction to Stream Processing and Apache Flink

• Stream processing vs batch processing: when to use each
• Apache Flink architecture: JobManager, TaskManager, and execution graph
• Flink deployment modes: local, standalone, YARN, and Kubernetes
• Setting up a Flink development environment with Java and Python

Module 2: Flink DataStream API Fundamentals

• Creating DataStream sources: file, socket, Kafka, and custom
• Transformations: map, filter, flatMap, keyBy, and reduce
• Sinks: writing to files, databases, Kafka, and Elasticsearch
• Parallelism configuration and operator chaining

Module 3: Windowing and Time Semantics

• Event time vs processing time vs ingestion time
• Watermarks: handling late data and out-of-order events
• Window types: tumbling, sliding, session, and global windows
• Window functions: reduce, aggregate, process, and apply

Module 4: Stateful Stream Processing

• Keyed state: ValueState, ListState, MapState, and ReducingState
• Operator state for non-keyed operations
• State backends: HashMap, RocksDB, and remote state storage
• State TTL configuration for automatic state expiry

Module 5: Fault Tolerance and Checkpointing

• Flink fault tolerance model: exactly-once and at-least-once semantics
• Checkpoint configuration: interval, timeout, and storage location
• Savepoints: manual snapshots for planned maintenance and upgrades
• Restart strategies: fixed delay, failure rate, and no restart

Module 6: Flink Table API and SQL

• Table API: relational operations on streams and batches
• Flink SQL: querying streaming data with familiar SQL syntax
• Connectors: Kafka, JDBC, filesystem, and Hive table sources
• Continuous queries: streaming aggregations and window joins

Module 7: Flink with Apache Kafka

• Kafka-Flink integration: source and sink connector configuration
• Exactly-once end-to-end processing with Kafka transactions
• Schema Registry integration for Avro and JSON schemas
• Monitoring Flink-Kafka pipelines with Flink Web UI and Prometheus

Module 8: Capstone Project and Assessment

• Real-time fraud detection pipeline using Flink and Kafka
• Windowed aggregation and stateful processing project
• Flink SQL streaming analytics application build
• Final assessment and course certification

Experience in the Industry Gain expertise from Apache Flink engineers who have built real-time stream processing pipelines for fraud detection, IoT analytics, recommendation systems, and operational intelligence at scale.

Backgrounds at the Top Our Flink trainers have worked at technology companies, financial institutions, and data engineering consultancies processing billions of events daily using Apache Flink on Kubernetes and cloud platforms.

Clear & Effective Teaching Flink DataStream API, windowing, stateful processing, checkpointing, Table API and SQL, and Kafka integration are taught progressively with real-time pipeline examples and hands-on coding labs.

Hands-On Learning Focus Students build real-time stream processing applications, implement stateful pipelines, configure fault tolerance, and deploy Flink jobs through comprehensive project-based labs.

Up-to-Date Knowledge Trainers keep content current with the latest Apache Flink releases, Flink CDC for change data capture, unified batch and streaming capabilities, and cloud-managed Flink services.