Items related to Spark: Big Data Cluster Computing in Production
Synopsis
Production-targeted Spark guidance with real-world use cases
Spark: Big Data Cluster Computing in Production goes beyond general Spark overviews to provide targeted guidance toward using lightning-fast big-data clustering in production. Written by an expert team well-known in the big data community, this book walks you through the challenges in moving from proof-of-concept or demo Spark applications to live Spark in production. Real use cases provide deep insight into common problems, limitations, challenges, and opportunities, while expert tips and tricks help you get the most out of Spark performance. Coverage includes Spark SQL, Tachyon, Kerberos, ML Lib, YARN, and Mesos, with clear, actionable guidance on resource scheduling, db connectors, streaming, security, and much more.
Spark has become the tool of choice for many Big Data problems, with more active contributors than any other Apache Software project. General introductory books abound, but this book is the first to provide deep insight and real-world advice on using Spark in production. Specific guidance, expert tips, and invaluable foresight make this guide an incredibly useful resource for real production settings.
- Review Spark hardware requirements and estimate cluster size
- Gain insight from real-world production use cases
- Tighten security, schedule resources, and fine-tune performance
- Overcome common problems encountered using Spark in production
Spark works with other big data tools including MapReduce and Hadoop, and uses languages you already know like Java, Scala, Python, and R. Lightning speed makes Spark too good to pass up, but understanding limitations and challenges in advance goes a long way toward easing actual production implementation. Spark: Big Data Cluster Computing in Production tells you everything you need to know, with real-world production insight and expert guidance, tips, and tricks.
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From the Back Cover
TIPS, TRICKS, AND SOLUTIONS FOR USING SPARK IN PRODUCTION
Spark's popularity means the field is expanding—in terms of both use and capability. Faster than Hadoop and MapReduce, but compatible with Java®, Scala, Python®, and R, this open source clustering framework is becoming a must-have skill. Spark: Big Data Cluster Computing in Production goes beyond the basics to show you how to bring Spark to real-world production environments. With expert instruction, real-life use cases, and frank discussion, this guide helps you move past the challenges and bring proof-of-concept Spark applications live.
- Fine-tune your Spark app to run on production data
- Manage resources, organize storage, and master monitoring
- Learn about potential problems from real-world use cases, and see where Spark fits best
- Estimate cluster size and nail down hardware requirements
- Tune up performance with memory management, partitioning, shuffling, and more
- Ensure data security with Kerberos
- Head off Spark streaming problems in production
- Integrate Spark with Yarn, Mesos, Tachyon, and more
About the Author
Ilya Ganelin is a data engineer working at Capital One Data Innovation Lab. Ilya is an active contributor to the core components of Apache Spark and a committer to Apache Apex.
Ema Orhian is a Big Data Engineer interested in scaling algorithms. She is the main committer on jaws-spark-sql-rest, a data warehouse explorer on top of Spark SQL.
Kai Sasaki is a software engineer working in distributed computing and machine learning. He is a Spark contributor who develops mainly MLlib, ML libraries.
Brennon York has been a core contributor to Apache Spark since 2014 including development on GraphX and the core build environment.
"About this title" may belong to another edition of this title.
- PublisherWiley india Pvt. Ltd
- ISBN 10 812656248X
- ISBN 13 9788126562480
- BindingPaperback
- LanguageEnglish
- Number of pages216
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