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Autor(en): 
  • Nuzhi Meyen
  • Building Data Pipelines Using Apache Beam 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 3 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   i.d.R. innert 7-14 Tagen versandfertig
    Veröffentlichung:  April 2026  
    Genre:  EDV / Informatik 
     
    Apache Beam / Batch and Streaming Processing / Data Pipelines
    ISBN:  9789349887879 
    EAN-Code: 
    9789349887879 
    Verlag:  Orange Education Pvt Ltd 
    Einband:  Kartoniert  
    Sprache:  English  
    Dimensionen:  H 235 mm / B 191 mm / D 19 mm 
    Gewicht:  654 gr 
    Seiten:  350 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    Build Data Pipelines that Survive Scale, Failure, and Change Book Description Building Data Pipelines Using Apache Beam provides a practical, production-focused guide to using Beam's unified programming model to write processing logic once, and run it across multiple runners, without rewriting core code. The book begins with the fundamentals of distributed data processing and Beam's core abstractions-PCollections, transforms, and pipeline design. You will then progress into stateful and stateless processing, event-time semantics, windows, triggers, watermarks, state, and timers-building the mental models required to reason about correctness at scale. What you will learn ¿ Design scalable batch and streaming pipelines with Apache Beam ¿ Implement event-time processing using windows, triggers, watermarks, state, and timers ¿ Build portable pipelines that execute consistently across multiple runners ¿ Apply advanced transformations and coders for efficient data processing ¿ Optimize pipelines for performance, latency, fault tolerance, and cost efficiency ¿ Deploy, monitor, debug, and operate production-grade data pipelines Who is This Book For? This book is tailored for Data Engineers, Senior Data Engineers, Analytics Engineers, Data Architects, and Platform Engineers who design, build, or operate batch and streaming data systems. Readers should be comfortable with Python or Java, SQL, and basic distributed system concepts such as parallelism, fault tolerance, event-time processing, and cloud-based data platforms. Table of Contents 1. Introduction to Apache Beam and Data Processing 2. Stateful and Stateless Processing with Apache Beam 3. Handling Event Time, Windows, and Triggers 4. Building Pipelines with Apache Beam 5. Transformations and Coders in Apache Beam 6. Advanced Pipeline Optimization Techniques 7. Deploying Apache Beam Pipelines on Different Runners 8. Monitoring, Debugging, and Tuning Apache Beam Pipelines 9. Case Studies: Apache Beam in the Real World Index

      



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