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Spark in Action, Second Edition: Covers Apache Spark 3 with Examples in Java, Python, and Scala

Product ID : 43947934


Galleon Product ID 43947934
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About Spark In Action, Second Edition: Covers Apache

Product Description Summary The Spark distributed data processing platform provides an easy-to-implement tool for ingesting, streaming, and processing data from any source. In Spark in Action, Second Edition, you’ll learn to take advantage of Spark’s core features and incredible processing speed, with applications including real-time computation, delayed evaluation, and machine learning. Spark skills are a hot commodity in enterprises worldwide, and with Spark’s powerful and flexible Java APIs, you can reap all the benefits without first learning Scala or Hadoop. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Analyzing enterprise data starts by reading, filtering, and merging files and streams from many sources. The Spark data processing engine handles this varied volume like a champ, delivering speeds 100 times faster than Hadoop systems. Thanks to SQL support, an intuitive interface, and a straightforward multilanguage API, you can use Spark without learning a complex new ecosystem. About the book Spark in Action, Second Edition, teaches you to create end-to-end analytics applications. In this entirely new book, you’ll learn from interesting Java-based examples, including a complete data pipeline for processing NASA satellite data. And you’ll discover Java, Python, and Scala code samples hosted on GitHub that you can explore and adapt, plus appendixes that give you a cheat sheet for installing tools and understanding Spark-specific terms. What's inside     Writing Spark applications in Java     Spark application architecture     Ingestion through files, databases, streaming, and Elasticsearch     Querying distributed datasets with Spark SQL About the reader This book does not assume previous experience with Spark, Scala, or Hadoop. About the author Jean-Georges Perrin is an experienced data and software architect. He is France’s first IBM Champion and has been honored for 12 consecutive years. Table of Contents PART 1 - THE THEORY CRIPPLED BY AWESOME EXAMPLES 1 So, what is Spark, anyway? 2 Architecture and flow 3 The majestic role of the dataframe 4 Fundamentally lazy 5 Building a simple app for deployment 6 Deploying your simple app PART 2 - INGESTION 7 Ingestion from files 8 Ingestion from databases 9 Advanced ingestion: finding data sources and building your own 10 Ingestion through structured streaming PART 3 - TRANSFORMING YOUR DATA 11 Working with SQL 12 Transforming your data 13 Transforming entire documents 14 Extending transformations with user-defined functions 15 Aggregating your data PART 4 - GOING FURTHER 16 Cache and checkpoint: Enhancing Spark’s performances 17 Exporting data and building full data pipelines 18 Exploring deployment Review "This book reveals the tools and secrets you need to drive innovation in your company or community."--Rob Thomas, IBM"An indispensable, well-paced, and in-depth guide. A must-have for anyone into big data and real-time stream processing."--Anupam Sengupta, GuardHat Inc."This book will help spark a love affair with distributed processing."--Conor Redmond, InComm Product Control"Currently the best book on the subject!"--Markus Breuer, Materna IPS"I am a big fan of your approach to Data Engineering, your book on Spark, and loved your talks... I am training Data Engineers at my company and your Spark in Action, 2e book is a mandatory material for it!"-- Thiago de Faria, LINKIT From the Author This project has been an effort spread over more than three years, with lots of energy spent in research, finding the right examples and use-cases, and, finally, enhancing the book based on the feedback from editors and reviewers. It has been intense. Spark in Action, Second Edition, is overflowing with illustrations with over 180 diagrams, labs, and real-life data sets including NASA; I deliberately focused on using examples that are very close to real-life datasets, to make sure