SummaryThe 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.Foreword by Rob Thomas.Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.About the technologyAnalyzing 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 bookSpark 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 insideWriting Spark applications in JavaSpark application architectureIngestion through files, databases, streaming, and ElasticsearchQuerying distributed datasets with Spark SQLAbout the readerThis book does not assume previous experience with Spark, Scala, or Hadoop.About the authorJean-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 ContentsPART 1 - THE THEORY CRIPPLED BY AWESOME EXAMPLES1 So, what is Spark, anyway?2 Architecture and flow3 The majestic role of the dataframe4 Fundamentally lazy5 Building a simple app for deployment6 Deploying your simple appPART 2 - INGESTION7 Ingestion from files8 Ingestion from databases9 Advanced ingestion: finding data sources and buildingyour own10 Ingestion through structured streamingPART 3 - TRANSFORMING YOUR DATA11 Working with SQL12 Transforming your data13 Transforming entire documents14 Extending transformations with user-defined functions15 Aggregating your dataPART 4 - GOING FURTHER16 Cache and checkpoint: Enhancing Spark’s performances17 Exporting data and building full data pipelines18 Exploring deployment