Course Introduction
Unified Batch and Streaming Data Engineering with Apache Beam Training Course provides a comprehensive framework for designing, developing, deploying, and operating modern data pipelines that process both bounded and unbounded datasets through a unified programming model. The course examines Apache Beam concepts, pipeline architecture, transformations, windowing, triggers, state management, and execution models while helping participants understand how to build reliable data engineering solutions that support changing enterprise data requirements.
This course develops practical expertise in Apache Beam’s programming abstractions and teaches participants how to translate business and analytical requirements into scalable data processing pipelines. Participants explore PCollections, ParDo, DoFns, composite transformations, grouping, combining, joins, side inputs, side outputs, coders, schemas, and pipeline options. Particular attention is given to designing reusable and maintainable pipelines that can operate consistently across batch and streaming workloads without creating unnecessary duplication between processing architectures.
The programme provides detailed coverage of streaming data engineering challenges, including event-time processing, watermarks, late-arriving data, windowing strategies, triggers, stateful processing, timers, deduplication, and exactly-once or effectively-once processing considerations. Participants learn how to reason about out-of-order events, unbounded data sources, replay scenarios, checkpointing, failure recovery, and changing data volumes while developing pipelines capable of delivering dependable results under real operational conditions.
The training also addresses the integration of Apache Beam with modern data platforms and execution environments. Participants examine how Beam pipelines can interact with messaging systems, cloud storage, relational databases, data warehouses, data lakes, streaming platforms, and cloud-native processing services. The course considers deployment patterns across different runners, including portable execution models, containerized environments, managed cloud services, and enterprise platforms, enabling participants to make informed architectural decisions based on scalability, performance, cost, security, and operational requirements.
Advanced engineering practices are incorporated throughout the course, including pipeline testing, performance optimization, observability, monitoring, logging, metrics, data quality controls, schema evolution, security, CI/CD, infrastructure automation, and production support. Emerging issues such as real-time analytics, event-driven architectures, lakehouse platforms, data products, AI and machine learning pipelines, generative AI data workloads, synthetic data, data governance, and increasingly complex hybrid architectures are also examined in relation to unified batch and streaming engineering.
By the end of the programme, participants will be equipped to design and manage enterprise-grade Apache Beam solutions that combine historical and real-time data processing within consistent engineering patterns. The course emphasizes practical problem solving, architectural reasoning, operational resilience, and lifecycle management so that organizations can reduce duplicated pipeline logic, improve data availability, strengthen processing reliability, and establish scalable foundations for analytics, reporting, machine learning, and real-time decision-support capabilities.
Duration
10 days
Who Should Attend
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Data Engineers responsible for designing, developing, and maintaining enterprise-scale batch and streaming data pipelines.
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Senior Data Engineers seeking advanced Apache Beam skills for unified data processing architectures.
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Data Architects designing modern platforms that combine historical, real-time, and event-driven data workloads.
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Cloud Data Engineers implementing scalable data processing solutions across public, private, or hybrid cloud environments.
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Analytics Engineers working with complex transformations and integrated batch and streaming data sources.
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Big Data Engineers responsible for distributed processing, data integration, and high-volume data workloads.
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Streaming Engineers developing real-time event processing and continuous data transformation solutions.
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Platform Engineers supporting data processing infrastructure, execution environments, and pipeline operations.
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Data Platform Developers building reusable data products and enterprise ingestion and transformation frameworks.
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Machine Learning Engineers integrating streaming and historical data pipelines into machine learning workflows.
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Data Scientists requiring reliable pipelines for feature generation, experimentation, model inputs, and real-time analytics.
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DevOps and MLOps professionals supporting automated deployment and operational management of data pipelines.
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Data Operations and Production Support teams responsible for monitoring, troubleshooting, and recovering data workloads.
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Technical Leads responsible for establishing engineering standards for scalable data processing initiatives.
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Solution Architects evaluating Apache Beam and related technologies for enterprise data modernization programmes.
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Technology Managers overseeing data engineering teams, platform investments, and transformation initiatives.
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Integration Specialists working with messaging platforms, APIs, databases, cloud storage, and enterprise applications.
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Professionals transitioning from traditional batch processing toward unified batch and streaming architectures.
Course Objectives
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Develop a comprehensive understanding of Apache Beam architecture, programming concepts, execution models, and the principles underlying unified batch and streaming data processing.
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Design scalable Apache Beam pipelines using PCollections, transformations, ParDo, DoFns, composite operations, grouping, combining, joins, and other core processing abstractions.
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Apply Apache Beam programming patterns to both bounded and unbounded datasets while maintaining reusable pipeline structures across different processing requirements.
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Design event-time processing solutions using windows, watermarks, triggers, state, and timers to manage delayed, duplicated, unordered, and continuously arriving events.
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Build robust streaming pipelines capable of handling changing data volumes, late data, processing failures, replay scenarios, and operational interruptions without compromising data reliability.
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Integrate Apache Beam pipelines with messaging platforms, cloud storage, databases, data warehouses, data lakes, APIs, and other enterprise data infrastructure components.
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Evaluate Apache Beam runners and deployment approaches according to workload characteristics, scalability requirements, performance expectations, operational complexity, and cost considerations.
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Implement data quality, validation, schema management, error handling, dead-letter processing, and monitoring mechanisms that improve the reliability of production data pipelines.
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Apply testing and software engineering practices to Beam pipelines, including unit testing, integration testing, pipeline validation, dependency management, version control, and automated deployment.
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Optimize pipeline performance by analyzing parallelism, worker utilization, data skew, serialization, memory consumption, I/O behavior, fusion, batching, and resource configuration.
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Establish secure and governable pipeline architectures that incorporate access controls, sensitive-data handling, metadata management, auditability, lineage, and enterprise operational standards.
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Design future-ready unified data engineering architectures supporting real-time analytics, AI and machine learning workloads, data products, lakehouse environments, and emerging event-driven enterprise applications.
Comprehensive Course Outline
Module 1: Foundations of Unified Batch and Streaming Data Engineering
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Evolution from traditional batch processing toward unified architectures capable of processing historical and continuously arriving enterprise data.
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Core characteristics of bounded and unbounded datasets and the architectural implications of processing each type at enterprise scale.
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Apache Beam architecture, design philosophy, programming model, portability principles, and role within modern data engineering ecosystems.
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Key use cases for unified processing, including analytics, operational reporting, event processing, machine learning, data integration, and real-time decision support.
Module 2: Apache Beam Architecture and Programming Model
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Understanding pipelines, PCollections, transforms, runners, pipeline options, coders, schemas, and execution concepts within Apache Beam.
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Designing Beam pipelines as reusable computational graphs that separate business transformation logic from execution infrastructure.
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Exploring Beam SDK capabilities, supported programming approaches, dependency management, and patterns for maintainable pipeline development.
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Understanding the relationship between Beam programming abstractions and distributed execution across local, cloud, and managed processing environments.
Module 3: Core Transformations and Data Processing Patterns
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Applying Map, FlatMap, Filter, ParDo, and DoFn patterns to perform scalable transformations across distributed datasets.
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Designing composite transformations that encapsulate reusable business logic and simplify complex multi-stage data processing workflows.
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Implementing GroupByKey, Combine, aggregation, joining, reshuffling, and other distributed processing patterns efficiently.
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Managing side inputs, side outputs, tagged outputs, branching pipelines, and conditional processing for complex enterprise data flows.
Module 4: Schemas, Types and Data Modeling
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Working with Beam schemas, typed data structures, coders, serialization, and data representations for reliable pipeline development.
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Designing consistent enterprise data models that support transformations across multiple source systems and downstream analytical applications.
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Managing schema evolution, compatibility, field changes, optional attributes, and data contracts within continuously operating pipelines.
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Applying schema-aware processing patterns to improve interoperability, maintainability, validation, and downstream data consumption.
Module 5: Batch Pipeline Engineering
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Designing scalable pipelines for historical, bounded datasets including extraction, transformation, enrichment, aggregation, validation, and loading.
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Developing batch processing workflows that efficiently handle large data volumes while controlling memory, compute, storage, and I/O requirements.
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Applying partitioning, parallel processing, intermediate storage, and optimized transformations to improve batch pipeline throughput.
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Establishing repeatable batch engineering patterns for data warehouse loading, lakehouse processing, migration workloads, and enterprise data integration.
Module 6: Streaming Pipeline Architecture
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Designing unbounded pipelines for continuously arriving events from messaging systems, applications, sensors, transactions, APIs, and operational platforms.
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Understanding streaming ingestion patterns, event serialization, message acknowledgement, delivery semantics, replay strategies, and failure recovery.
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Developing resilient streaming transformations that process events continuously while maintaining operational stability under fluctuating workloads.
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Designing streaming architectures that support real-time dashboards, alerts, operational analytics, event enrichment, and downstream data products.
Module 7: Event Time, Processing Time and Windowing
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Understanding event time and processing time and applying the distinction to accurate processing of continuously arriving and delayed events.
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Designing fixed, sliding, session, and custom windows for analytical and operational streaming use cases with different temporal requirements.
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Working with watermarks to estimate event completeness and balance result timeliness against the arrival of late or out-of-order records.
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Developing windowing strategies that support aggregation, trend analysis, sessionization, anomaly detection, and real-time business metrics.
Module 8: Triggers, State and Timers
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Designing trigger strategies that determine when streaming results should be emitted, updated, finalized, or repeatedly recalculated.
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Applying stateful processing to maintain contextual information across events and support complex event-processing requirements.
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Using timers for time-dependent processing logic, session management, delayed actions, and sophisticated streaming workflow behavior.
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Managing state growth, expiration, recovery, consistency, and performance considerations in long-running production streaming pipelines.
Module 9: Data Sources, Sinks and Enterprise Integration
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Integrating Apache Beam with cloud object storage, relational databases, data warehouses, data lakes, APIs, and enterprise application platforms.
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Connecting Beam pipelines to messaging and event platforms while designing reliable ingestion, transformation, acknowledgement, and delivery workflows.
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Implementing reusable connectors and I/O patterns that accommodate heterogeneous enterprise environments and changing technology landscapes.
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Designing integration architectures that support interoperability between operational systems, analytical platforms, streaming infrastructure, and emerging data products.
Module 10: Reliability, Data Quality and Error Handling
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Designing validation rules, quality checks, exception handling, dead-letter patterns, and quarantine mechanisms for unreliable or malformed data.
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Managing duplicates, missing records, invalid schemas, unexpected values, corrupt messages, and other data quality challenges in distributed pipelines.
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Implementing retry strategies, failure isolation, recovery workflows, and replay mechanisms that improve operational resilience.
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Establishing measurable data quality controls that connect pipeline reliability with business-critical accuracy, completeness, timeliness, and consistency requirements.
Module 11: Testing, CI/CD and Data Engineering Practices
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Developing unit, integration, and end-to-end tests for Beam transformations, pipeline components, data quality rules, and streaming behaviors.
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Applying software engineering principles including modularity, code review, dependency management, version control, documentation, and reusable components.
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Establishing CI/CD pipelines for automated testing, packaging, deployment, validation, rollback, and controlled promotion of data processing workloads.
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Managing development environments and release processes that enable multiple engineering teams to collaborate safely on enterprise Beam applications.
Module 12: Performance Engineering and Scalability
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Analyzing pipeline execution behavior, parallelism, worker utilization, data movement, serialization, fusion, and resource consumption to identify bottlenecks.
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Managing data skew, hot keys, expensive transformations, inefficient joins, excessive shuffling, and other distributed processing performance challenges.
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Optimizing throughput and latency for both batch and streaming workloads while balancing infrastructure capacity, reliability, and operating costs.
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Establishing performance benchmarking and capacity planning practices for pipelines expected to handle rapidly growing data volumes and workload variability.
Module 13: Apache Beam Runners and Deployment Architecture
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Understanding the role of runners and selecting appropriate execution environments based on workload, platform, operational, security, and scalability requirements.
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Comparing local development, portable execution, managed cloud processing, containerized deployment, and enterprise production execution approaches.
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Designing deployment architectures that separate pipeline code from infrastructure configuration and enable consistent execution across environments.
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Managing portability considerations, runner-specific capabilities, operational dependencies, and migration requirements when changing execution platforms.
Module 14: Observability, Monitoring and Production Operations
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Implementing logging, metrics, monitoring, tracing, pipeline health indicators, and operational dashboards for production Beam workloads.
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Establishing alerting mechanisms for failures, processing delays, throughput degradation, data quality issues, resource exhaustion, and abnormal pipeline behavior.
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Designing operational runbooks covering incident response, troubleshooting, replay, recovery, scaling, maintenance, and controlled pipeline restarts.
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Applying data lineage, metadata, observability, and operational intelligence practices to improve transparency across increasingly complex data processing environments.
Module 15: Security, Governance and Enterprise Data Management
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Applying identity, access control, encryption, secrets management, network security, and secure configuration practices to enterprise Beam deployments.
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Protecting sensitive, confidential, regulated, and personally identifiable information throughout ingestion, transformation, storage, monitoring, and delivery.
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Integrating pipeline governance with metadata management, lineage, data classification, retention requirements, auditability, and enterprise data policies.
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Addressing emerging governance requirements for AI-enabled data pipelines, synthetic data, cross-border processing, data products, and increasingly automated data operations.
Module 16: Advanced Unified Data Engineering and Emerging Architectures
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Designing unified batch and streaming architectures for lakehouse platforms, real-time analytics, event-driven systems, operational intelligence, and enterprise data products.
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Integrating Beam-based pipelines with machine learning and AI workflows, including feature engineering, real-time inference inputs, model data preparation, and generative AI data pipelines.
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Evaluating emerging patterns such as data mesh, domain-oriented data products, real-time lakehouses, streaming ETL, intelligent automation, and increasingly event-driven enterprise architectures.
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Developing an enterprise roadmap for Apache Beam adoption, modernization, platform standardization, operational maturity, skills development, and sustainable unified data engineering practices.
Training Approach
This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.
Tailor-Made Course
This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808
Training Venue
The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.
Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant
Certification
Participants will be issued with Upskill certificate upon completion of this course.
Airport Pickup and Accommodation
Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808
Terms of Payment:
Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.
