Overview
- 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
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Table of contents (15 chapters)
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Front Matter
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The Fundamentals of Data Engineering with Spark
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Front Matter
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Back Matter
Keywords
- Apache Spark
- Data Engineering
- Data Modeling
- Data Pipelines
- Data Architecture
- Streaming Data
- Big Data
- Machine Learning
- ETL
- Data Lineage
- Analytics
- Big Data
- Stream Processing with Apache Spark
- Data-Intensive Applications
- Modern Data Engineering
- Apache Zeppelin
- DataFrames
- SparkSQL
- Design Patterns for Data
- Performance Tuning
About this book
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
Authors and Affiliations
About the author
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.
Bibliographic Information
Book Title: Modern Data Engineering with Apache Spark
Book Subtitle: A Hands-On Guide for Building Mission-Critical Streaming Applications
Authors: Scott Haines
DOI: https://doi.org/10.1007/978-1-4842-7452-1
Publisher: Apress Berkeley, CA
eBook Packages: Professional and Applied Computing, Apress Access Books, Professional and Applied Computing (R0)
Copyright Information: Scott Haines 2022
Softcover ISBN: 978-1-4842-7451-4Published: 23 March 2022
eBook ISBN: 978-1-4842-7452-1Published: 22 March 2022
Edition Number: 1
Number of Pages: XXV, 585
Number of Illustrations: 59 b/w illustrations
Topics: Java, Statistics, general, Database Management, Data Mining and Knowledge Discovery