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Apache Beam Mastery

Course Level: Intermediate to Advanced Prerequisites: Java or Python, distributed processing concepts Duration: 26-30 hours Hands-on Projects: 16+ portable data processing pipelines

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What You’ll Master

Apache Beam provides a unified programming model for batch and streaming data processing, with portability across multiple execution engines (runners). Write your pipeline once, run it on Spark, Flink, Google Cloud Dataflow, or any compatible runner. You’ll gain deep expertise in:
  • Unified Programming Model: Single API for batch and streaming
  • Beam Model Foundations: From the Dataflow Model paper (Google)
  • Windowing & Triggers: Advanced time-based processing
  • Portable Pipelines: Multi-runner compatibility
  • Beam SQL: Declarative data processing
  • Beam I/O Connectors: Integration with dozens of data sources
  • Production Patterns: Testing, monitoring, and deployment
This course covers Apache Beam 2.x with examples in both Java SDK and Python SDK, emphasizing portability and the unified model.

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Course Structure

Duration: 3-4 hours | Foundation ModuleUnderstand the foundational concepts from Google’s Dataflow Model paper that inspired Apache Beam.What You’ll Learn:
  • The problem of incompatible batch/streaming APIs
  • Deep dive into the Dataflow Model paper
  • What, Where, When, How: The four questions framework
  • Event time vs processing time
  • Watermarks and triggers
  • Evolution from Google Dataflow to Apache Beam
Key Topics:
  • Unified model: batch is bounded streaming
  • PCollection abstraction
  • Transforms and PTransforms
  • Windowing strategies
  • Triggers for result materialization
  • Accumulation modes
Beam Philosophy:
  • Write once, run anywhere
  • Runner independence
  • Portability framework
Start Learning →
Duration: 4-5 hours | Core ModuleMaster the fundamental Beam constructs: PCollections, ParDo, and pipeline composition.What You’ll Learn:
  • Creating pipelines and PCollections
  • ParDo for element-wise transformations
  • DoFn lifecycle and annotations
  • Combine for aggregations
  • Flatten and Partition
  • Composite transforms
  • Side inputs and side outputs
Hands-on Labs:
  • WordCount in Java and Python
  • ETL pipeline with data validation
  • Custom DoFns with state and timers
  • Multi-output transformations
  • Implementing reusable composite transforms
Code Examples:
  • Java SDK complete examples
  • Python SDK parallel implementations
  • Cross-language transforms
Core Programming →
Duration: 4-5 hours | Core ModuleImplement sophisticated windowing strategies for time-based processing.What You’ll Learn:
  • Fixed (tumbling) windows
  • Sliding windows
  • Session windows
  • Global windows
  • Custom windows
  • Window merging and assigners
  • Timestamp manipulation
Advanced Windowing:
  • Combining window types
  • Window transformation patterns
  • Window-preserving vs window-erasing transforms
  • Working with multiple windows
Real-World Scenarios:
  • Time-series aggregations
  • User session analysis
  • Traffic pattern detection
  • Metrics collection and rollups
Master Windowing →
Duration: 4-5 hours | Advanced ModuleControl when results are emitted with triggers and handle late data with watermarks.What You’ll Learn:
  • Default trigger behavior
  • Early and late firings
  • Repeatedly firing triggers
  • Composite triggers (AfterWatermark, AfterProcessingTime, AfterPane)
  • Allowed lateness
  • Accumulation modes: Discarding, Accumulating, Accumulating & Retracting
Trigger Patterns:
  • Speculative early results
  • Refinement with late data
  • Complex trigger combinations
  • Custom triggers
Watermark Strategies:
  • Understanding watermark propagation
  • Custom watermark estimators
  • Dealing with straggling data
  • Watermark holds
Advanced Triggers →
Duration: 4-5 hours | Advanced ModuleBuild stateful processing with user-defined state and timers.What You’ll Learn:
  • State API: ValueState, BagState, MapState, SetState
  • CombiningState for efficient aggregations
  • Timers API: event-time and processing-time timers
  • State and timer interaction patterns
  • State expiration and cleanup
  • Cross-window state
Advanced State Patterns:
  • Custom sessionization logic
  • Approximate algorithms with state (HyperLogLog, Count-Min Sketch)
  • Exactly-once state updates
  • State migration
Hands-on Projects:
  • Fraud detection with stateful rules
  • Recommendation system with user state
  • Complex event processing
  • Deduplication with state and timers
Stateful Processing →
Duration: 3-4 hours | Integration ModuleConnect to various data sources and sinks with Beam I/O connectors.What You’ll Learn:
  • Built-in I/O connectors: File, Kafka, BigQuery, JDBC, etc.
  • Reading from bounded sources (batch)
  • Reading from unbounded sources (streaming)
  • Writing to sinks with windowing
  • Dynamic destinations
  • Custom I/O transforms
  • Splittable DoFn for custom sources
Supported I/O:
  • File systems: Text, Avro, Parquet, JSON
  • Messaging: Kafka, Pub/Sub, Kinesis, RabbitMQ
  • Databases: JDBC, MongoDB, Cassandra, HBase
  • Cloud: BigQuery, Bigtable, S3, GCS
  • Search: Elasticsearch, Solr
Advanced I/O:
  • Implementing custom sources and sinks
  • Bounded vs unbounded source characteristics
  • Checkpointing and offset management
  • Schema handling and evolution
I/O Connectors →
Duration: 3-4 hours | SQL ModuleProcess data declaratively with Beam SQL and schema-aware PCollections.What You’ll Learn:
  • Schema-aware PCollections
  • Row class and schema definition
  • Beam SQL syntax
  • Joins in SQL
  • Aggregations and windowing in SQL
  • User-defined functions (UDFs)
  • Integration with Table API
SQL Features:
  • Streaming SQL queries
  • Complex transformations in SQL
  • Combining SQL with dataflow programming
  • Schema evolution
Hands-on Examples:
  • ETL pipelines in SQL
  • Stream joins with Beam SQL
  • Analytical queries on streaming data
  • Migrating from batch SQL to streaming
Beam SQL →
Duration: 3-4 hours | Operations ModuleRun Beam pipelines on different runners and implement comprehensive testing.What You’ll Learn:
  • Runner capabilities and compatibility
  • DirectRunner for local testing
  • FlinkRunner configuration and deployment
  • SparkRunner setup and tuning
  • DataflowRunner for Google Cloud
  • Runner-specific optimizations
Testing Strategies:
  • Unit testing with DirectRunner
  • Integration testing patterns
  • PAssert for pipeline validation
  • TestStream for streaming tests
  • Mocking I/O in tests
Deployment Patterns:
  • Packaging for different runners
  • Resource requirements and configuration
  • Monitoring and metrics
  • Template deployment
Multi-Runner Setup →
Duration: 4-5 hours | Comprehensive ProjectBuild a portable ETL pipeline that can run on multiple clouds and execution engines.Project Overview: Build a data processing pipeline that ingests from multiple sources, performs transformations, and writes to various destinations - deployable on any runner.Components:
  • Multi-source ingestion (Kafka, files, API)
  • Schema validation and transformation
  • Windowed aggregations with late data handling
  • Enrichment with side inputs
  • Multi-destination output
  • Comprehensive testing suite
  • Deployment configs for Flink, Spark, and Dataflow
Skills Demonstrated:
  • Portable pipeline design
  • Multi-runner compatibility
  • Advanced windowing and triggers
  • Stateful processing
  • Testing and validation
  • Production deployment
Build Project →

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Why Learn Beam?

Write Once, Run Anywhere

Single codebase runs on Spark, Flink, Dataflow, or any runner - avoid vendor lock-in.

Unified Model

One API for batch and streaming - learn once, handle all processing patterns.

Google Heritage

Based on Google’s Dataflow Model - battle-tested at massive scale.

Multi-Language

Java, Python, Go SDKs with cross-language transforms - use the best tool for each job.

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Course Philosophy

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1. Dataflow Model First

Start with the foundational “What/Where/When/How” framework from the Dataflow Model paper.

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2. Multi-Language Examples

Every concept shown in both Java SDK and Python SDK for maximum accessibility.

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3. Runner Portability

Emphasis on writing runner-independent code that works everywhere.

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4. Production Patterns

Not just toy examples - production-ready patterns with testing and deployment.

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Prerequisites

Before starting this course, you should have:
  • Programming: Java 11+ or Python 3.7+ experience
  • Distributed Processing: Basic understanding (we teach Beam-specific concepts)
  • SQL: Helpful for Beam SQL module
  • Build Tools: Maven/Gradle (Java) or pip (Python)
Beam’s abstraction layer means you don’t need deep knowledge of Spark or Flink - we’ll teach the Beam model!

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Learning Resources

Throughout this course, we reference:
  • Research Papers:
    • “The Dataflow Model: A Practical Approach to Balancing Correctness, Latency, and Cost in Massive-Scale, Unbounded, Out-of-Order Data Processing” (Akidau et al., 2015)
    • Related papers on windowing, triggers, and watermarks
  • Official Documentation: Apache Beam docs and programming guides
  • Books: “Streaming Systems” by Akidau et al. (written by Beam creators)
  • Code Repository: All examples for Java and Python SDKs

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Beam vs Other Frameworks

FeatureBeamSparkFlink
PortabilityMulti-runnerSpark-onlyFlink-only
ModelUnified batch/streamMicro-batch primaryStreaming primary
LanguageJava, Python, GoScala, Python, JavaJava, Scala
Cloud NativeYes (Dataflow)EMR, DatabricksKinesis Analytics
Learning CurveMediumMediumMedium-High
When to Choose Beam:
  • Need portability across runners
  • Want unified batch/streaming code
  • Building cloud-agnostic pipelines
  • Leveraging Google Cloud Dataflow
  • Working in Python-heavy organizations

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Ready to Begin?

Start your journey into portable data processing.

Module 1: Introduction & The Dataflow Model

Begin with the foundational Dataflow Model concepts

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Course Outcomes

By completing this course, you’ll be able to:
  • Design portable batch and streaming pipelines
  • Master the What/Where/When/How framework
  • Implement advanced windowing and trigger strategies
  • Build stateful processing with state and timers
  • Use Beam SQL for declarative transformations
  • Deploy pipelines on multiple runners (Spark, Flink, Dataflow)
  • Test pipelines comprehensively
  • Integrate with dozens of data sources and sinks
  • Choose the right runner for your requirements
Estimated Time to Complete: 26-30 hours of focused learning Recommended Pace: 2 modules per week