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Apache Flink Mastery

Course Level: Intermediate to Advanced Prerequisites: Java/Scala basics, distributed systems, streaming concepts Duration: 28-32 hours Hands-on Projects: 18+ real-time streaming projects

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What You’ll Master

Apache Flink is the industry’s leading framework for stateful computations over unbounded and bounded data streams - designed from the ground up for true stream processing (not micro-batching). You’ll gain deep expertise in:
  • Stream Processing Foundations: Event time vs processing time, watermarks, windowing
  • Stateful Computations: Managed state, checkpointing, savepoints
  • Exactly-Once Semantics: Distributed snapshots algorithm (Chandy-Lamport)
  • DataStream API: Low-level stream processing with full control
  • Table API & SQL: High-level declarative stream processing
  • CEP (Complex Event Processing): Pattern detection in event streams
  • Production Operations: State backends, deployment, fault tolerance
This course covers Flink 1.18+ with emphasis on both streaming fundamentals (from research papers) and production deployments.

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Course Structure

Duration: 3-4 hours | Foundation ModuleUnderstand the fundamental differences between batch and stream processing, and why Flink treats batch as a special case of streaming.What You’ll Learn:
  • The limitations of micro-batching (Spark Streaming)
  • Deep dive into the Dataflow Model paper (Google)
  • Understanding event time, processing time, and ingestion time
  • Watermarks and late data handling
  • Flink’s architecture: JobManager, TaskManager, parallelism
Key Topics:
  • Stream vs batch processing paradigms
  • Out-of-order and late events
  • Exactly-once vs at-least-once semantics
  • Chandy-Lamport distributed snapshots algorithm
  • Flink’s position in the streaming landscape
Start Learning →
Duration: 4-5 hours | Core ModuleMaster the low-level DataStream API for fine-grained control over stream processing.What You’ll Learn:
  • Creating DataStreams from various sources (Kafka, files, sockets)
  • Basic transformations: map, flatMap, filter, keyBy
  • Stateful transformations: mapWithState, flatMapWithState
  • Rich functions and ProcessFunction
  • Side outputs for multi-stream patterns
  • Async I/O for external enrichment
Hands-on Labs:
  • Real-time ETL pipeline
  • Stateful stream processing (e.g., running averages)
  • Stream joins and CoProcessFunction
  • Broadcast state pattern
  • Custom source and sink implementations
Deep Dive →
Duration: 4-5 hours | Core ModuleMaster event time processing and watermark strategies for handling out-of-order events.What You’ll Learn:
  • Event time assignment strategies
  • Watermark generation: periodic vs punctuated
  • Allowed lateness and side outputs for late data
  • Timestamp extractors and watermark strategies
  • Idleness detection for low-throughput streams
Advanced Topics:
  • Multi-source watermark propagation
  • Custom watermark strategies
  • Dealing with data skew in event time
  • Watermark alignment
Practical Scenarios:
  • IoT sensor data with clock drift
  • Log aggregation from distributed systems
  • Financial transaction processing
Master Event Time →
Duration: 4-5 hours | Core ModuleImplement sophisticated windowing logic for time-based aggregations.What You’ll Learn:
  • Window types: Tumbling, Sliding, Session, Global
  • Window assigners and triggers
  • Evictors for custom window logic
  • ProcessWindowFunction vs AggregateFunction
  • Incremental vs full-window aggregations
Advanced Windowing:
  • Custom window assigners
  • Early firing and speculative results
  • Allowed lateness configuration
  • Window joins
Real-World Projects:
  • Real-time analytics dashboards
  • Sessionization of user activity
  • Anomaly detection with sliding windows
Window Processing →
Duration: 5-6 hours | Advanced ModuleBuild stateful applications with managed state, checkpointing, and fault tolerance.What You’ll Learn:
  • State types: ValueState, ListState, MapState, ReducingState
  • Keyed state vs operator state
  • State backends: Memory, RocksDB, custom
  • Checkpointing and recovery
  • Savepoints for application versioning
  • State TTL for automatic cleanup
Advanced State Management:
  • Queryable state for external access
  • State migration and schema evolution
  • Incremental checkpointing with RocksDB
  • State processor API for offline state manipulation
Hands-on Projects:
  • Fraud detection with stateful rules
  • User profile enrichment
  • Deduplication with state
  • Complex aggregations across time
Stateful Processing →
Duration: 3-4 hours | Pattern ModuleDetect complex patterns in event streams with Flink CEP.What You’ll Learn:
  • Pattern API basics
  • Individual patterns: simple, looping, combining
  • Pattern sequences and groups
  • Quantifiers: oneOrMore, times, optional
  • Conditions: where, or, until, within
  • Selecting and timeout handling
Advanced Patterns:
  • Iterative patterns
  • After match skip strategies
  • Combining patterns with AND, OR
  • Dynamic patterns from configuration
Use Cases:
  • Fraud detection (suspicious transaction patterns)
  • System monitoring (failure sequence detection)
  • Trading signals (price pattern recognition)
Pattern Detection →
Duration: 4-5 hours | Operations ModuleDeploy and operate Flink in production with high availability and monitoring.What You’ll Learn:
  • Deployment modes: Standalone, YARN, Kubernetes
  • High availability with ZooKeeper/Kubernetes
  • Resource management and slot allocation
  • Monitoring with metrics reporters
  • Backpressure handling
  • Failure recovery and restart strategies
Production Patterns:
  • Blue-green deployments with savepoints
  • Application mode vs session mode
  • Containerization with Docker
  • Scaling strategies
Operational Skills:
  • Performance tuning
  • Debugging with Flink UI
  • Log aggregation
  • Cost optimization
Deploy to Production →
Duration: 5-6 hours | Comprehensive ProjectBuild a production-ready fraud detection system processing financial transactions.Project Components:
  • Kafka integration for transaction streams
  • Stateful rule engine with Flink state
  • CEP for pattern-based fraud detection
  • Machine learning model serving
  • Real-time alerting to downstream systems
  • Monitoring dashboard
Skills Demonstrated:
  • Event time processing with watermarks
  • Stateful computations
  • Pattern detection
  • Exactly-once semantics
  • Production deployment
Build Project →

True Streaming

Row-by-row processing with millisecond latency, not micro-batches. Ideal for real-time use cases.

Exactly-Once Semantics

Built-in distributed snapshots ensure exactly-once state consistency and processing guarantees.

Stateful Processing

First-class support for large-scale stateful computations with efficient managed state.

Batch as Streaming

Unified engine treats batch as bounded streams - one API for both paradigms.

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Prerequisites

  • Programming: Java or Scala (all examples in both languages)
  • Distributed Systems: Understanding of distributed computing concepts
  • Streaming Basics: Event-driven architectures (we’ll teach the rest)
  • SQL: For Table API/SQL modules

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Ready to Begin?

Module 1: Introduction & Streaming Foundations

Start with the fundamentals of true stream processing