> ## Documentation Index
> Fetch the complete documentation index at: https://resources.devweekends.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Apache Flink Mastery

> Master true stream processing with Apache Flink - from stateful computations to exactly-once guarantees

# Apache Flink Mastery

> [!NOTE]
>
> **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

## 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

> [!NOTE]
>
> This course covers Flink 1.18+ with emphasis on both streaming fundamentals (from research papers) and production deployments.

## Course Structure

> [accordion] Module 1: Introduction & Streaming Foundations
>
> **Duration**: 3-4 hours | **Foundation Module**
>
> Understand 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 →](https://resources.devweekends.com/distributed-systems-tools/flink-introduction.md)

> [accordion] Module 2: DataStream API & Transformations
>
> **Duration**: 4-5 hours | **Core Module**
>
> Master 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 →](https://resources.devweekends.com/distributed-systems-tools/flink-datastream.md)

> [accordion] Module 3: Event Time & Watermarks
>
> **Duration**: 4-5 hours | **Core Module**
>
> Master 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 →](https://resources.devweekends.com/distributed-systems-tools/flink-eventtime.md)

> [accordion] Module 4: Windows & Time-Based Operations
>
> **Duration**: 4-5 hours | **Core Module**
>
> Implement 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 →](https://resources.devweekends.com/distributed-systems-tools/flink-windows.md)

> [accordion] Module 5: Stateful Stream Processing
>
> **Duration**: 5-6 hours | **Advanced Module**
>
> Build 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 →](https://resources.devweekends.com/distributed-systems-tools/flink-state.md)

> [accordion] Module 6: Table API & Flink SQL
>
> **Duration**: 4-5 hours | **SQL Module**
>
> Use high-level declarative APIs for stream processing with SQL.
>
> **What You'll Learn**:
>
> * Table API fundamentals
> * Flink SQL syntax for streaming queries
> * Dynamic tables and continuous queries
> * Catalogs and metadata management
> * User-defined functions (UDFs, UDAFs, UDTFs)
> * Temporal tables and versioned joins
>
> **Advanced SQL**:
>
> * Windowed aggregations in SQL
> * Pattern recognition with MATCH\_RECOGNIZE
> * Deduplication and top-N queries
> * Changelog streams and upsert mode
>
> **Integration**:
>
> * Connecting to external systems (Kafka, JDBC, Elasticsearch)
> * Hive integration for unified batch/streaming
> * Schema evolution and format handling
>
> [SQL for Streams →](https://resources.devweekends.com/distributed-systems-tools/flink-sql.md)

> [accordion] Module 7: Complex Event Processing (CEP)
>
> **Duration**: 3-4 hours | **Pattern Module**
>
> Detect 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 →](https://resources.devweekends.com/distributed-systems-tools/flink-cep.md)

> [accordion] Module 8: Production Deployment & Operations
>
> **Duration**: 4-5 hours | **Operations Module**
>
> Deploy 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 →](https://resources.devweekends.com/distributed-systems-tools/flink-operations.md)

> [accordion] Capstone Project: Real-Time Fraud Detection System
>
> **Duration**: 5-6 hours | **Comprehensive Project**
>
> Build 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 →](https://resources.devweekends.com/distributed-systems-tools/flink-capstone.md)

## Why Learn Flink?

> [card] True Streaming
>
> Row-by-row processing with millisecond latency, not micro-batches. Ideal for real-time use cases.

> [card] Exactly-Once Semantics
>
> Built-in distributed snapshots ensure exactly-once state consistency and processing guarantees.

> [card] Stateful Processing
>
> First-class support for large-scale stateful computations with efficient managed state.

> [card] Batch as Streaming
>
> Unified engine treats batch as bounded streams - one API for both paradigms.

## 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

## Ready to Begin?

> [card] [Module 1: Introduction & Streaming Foundations](https://resources.devweekends.com/distributed-systems-tools/flink-introduction.md)
>
> Start with the fundamentals of true stream processing


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