Modern Data Engineering with Apache Spark

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Sprache: Englisch

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Beschreibung

Leverage Apache Spark within a modern data engineering ecosystem. This hands-on guide will teach you how to write fully functional applications, follow industry best practices, and learn the rationale behind these decisions. With Apache Spark as the foundation, you will follow a step-by-step journey beginning with the basics of data ingestion, processing, and transformation, and ending up with an entire local data platform running Apache Spark, Apache Zeppelin, Apache Kafka, Redis, MySQL, Minio (S3), and Apache Airflow.

Apache Spark applications solve a wide range of data problems from traditional data loading and processing to rich SQL-based analysis as well as complex machine learning workloads and even near real-time processing of streaming data. Spark fits well as a central foundation for any data engineering workload. This book will teach you to write interactive Spark applications using Apache Zeppelin notebooks, write and compilereusable applications and modules, and fully test both batch and streaming. You will also learn to containerize your applications using Docker and run and deploy your Spark applications using a variety of tools such as Apache Airflow, Docker and Kubernetes.

Reading this book will empower you to take advantage of Apache Spark to optimize your data pipelines and teach you to craft modular and testable Spark applications. You will create and deploy mission-critical streaming spark applications in a low-stress environment that paves the way for your own path to production.

What You Will Learn

Simplify data transformation with Spark Pipelines and Spark SQL
Bridge data engineering with machine learning
Architect modular data pipeline applications
Build reusable application components and libraries
Containerize your Spark applications for consistency and reliability
Use Docker and Kubernetes to deploy your Spark applications
Speed up application experimentation using Apache Zeppelin and Docker
Understand serializable structured data and data contracts
Harness effective strategies for optimizing data in your data lakes
Build end-to-end Spark structured streaming applications using Redis and Apache Kafka
Embrace testing for your batch and streaming applications
Deploy and monitor your Spark applications

Who This Book Is For

Professional software engineers who want to take their current skills and apply them to new and exciting opportunities within the data ecosystem, practicing data engineers who are looking for a guiding light while traversing the many challenges of moving from batch to streaming modes, data architects who wish to provide clear and concise direction for how best to harness anduse Apache Spark within their organization, and those interested in the ins and outs of becoming a modern data engineer in today's fast-paced and data-hungry world

What You Will Learn

Simplify data transformation with Spark Pipelines and Spark SQL
Bridge data engineering with machine learning
Architect modular data pipeline applications
Build reusable application components and libraries
Containerize your Spark applications for consistency and reliability
Use Docker and Kubernetes to deploy your Spark applications
Speed up application experimentation using Apache Zeppelin and Docker
Understand serializable structured data and data contracts
Harness effective strategies for optimizing data in your data lakes
Build end-to-end Spark structured streaming applications using Redis and Apache Kafka
Embrace testing for your batch and streaming applications
Deploy and monitor your Spark applications

Who This Book Is For

Über den Autor

Scott Haines is a full stack engineer with a current focus on real-time, highly available, trustworthy analytics systems. He works at Twilio as a Principal Software Engineer on the Voice Insights team, where he helps drive Spark adoption, creates streaming pipeline architectures, and helps to architect and build out a massive stream and batch processing platform.

Prior to Twilio, Scott worked writing the backend Java APIs for Yahoo Games as well as the real-time game ranking and ratings engine (built on Storm) to provide personalized recommendations and page views for 10 million customers. He finished his tenure at Yahoo working for Flurry Analytics where he wrote the alerts and notifications system for mobile devices.

Zusammenfassung

Provides a practical approach to data engineering through the lens of Apache Spark

Includes lessons from the author's experience in managing massive data pipelines

Gives you a toolbox of solutions to draw on when solving future problems

Inhaltsverzeichnis

Part I. The Fundamentals of Data Engineering with Spark.- 1. Introduction to Modern Data Engineering.- 2. Getting Started with Apache Spark.- 3. Working with Data.- 4. Transforming Data with Spark SQL and the DataFrame API.- 5. Bridging Spark SQL with JDBC.- 6. Data Discovery and the Spark SQL Catalog.- 7. Data Pipelines & Structured Spark Applications.- Part II. The Streaming Pipeline Ecosystem.- 8. Workflow Orchestration with Apache Airflow.- 9. A Gentle Introduction to Stream Processing.- 10. Patterns for Writing Structured Streaming Applications.- 11. Apache Kafka & Spark Structured Streaming.- 12. Analytical Processing & Insights.- Part III. Advanced Techniques.- 13. Advanced Analytics with Spark Stateful Structured Streaming.- 14. Deploying Mission Critical Spark Applications on Spark Standalone.- 15. Deploying Mission Critical Spark Applications on Kubernetes.

Details

Erscheinungsjahr:	2022
Genre:	Importe , Informatik
Rubrik:	Naturwissenschaften & Technik
Medium:	Taschenbuch
Inhalt:	xxv 585 S. 59 s/w Illustr. 585 p. 59 illus.
ISBN-13:	9781484274514
ISBN-10:	1484274512
Sprache:	Englisch
Einband:	Kartoniert / Broschiert
Autor:	Haines, Scott
Hersteller:	Apress Apress L.P.
Verantwortliche Person für die EU:	APress in Springer Science + Business Media, Heidelberger Platz 3, D-14197 Berlin, juergen.hartmann@springer.com
Maße:	254 x 178 x 32 mm
Von/Mit:	Scott Haines
Erscheinungsdatum:	23.03.2022
Gewicht:	1,134 kg