Advanced analytics on your Big Data with latest Apache Spark 2.x

About This Book

• An advanced guide with a combination of instructions and practical examples to extend the most up-to date Spark functionalities.
• Extend your data processing capabilities to process huge chunk of data in minimum time using advanced concepts in Spark.
• Master the art of real-time processing with the help of Apache Spark 2.x

Who This Book Is For

If you are a developer with some experience with Spark and want to strengthen your knowledge of how to get around in the world of Spark, then this book is ideal for you. Basic knowledge of Linux, Hadoop and Spark is assumed. Reasonable knowledge of Scala is expected.

What You Will Learn

• Examine Advanced Machine Learning and DeepLearning with MLlib, SparkML, SystemML, H2O and DeepLearning4J
• Study highly optimised unified batch and real-time data processing using SparkSQL and Structured Streaming
• Evaluate large-scale Graph Processing and Analysis using GraphX and GraphFrames
• Apply Apache Spark in Elastic deployments using Jupyter and Zeppelin Notebooks, Docker, Kubernetes and the IBM Cloud
• Understand internal details of cost based optimizers used in Catalyst, SystemML and GraphFrames
• Learn how specific parameter settings affect overall performance of an Apache Spark cluster
• Leverage Scala, R and python for your data science projects

In Detail

Apache Spark is an in-memory cluster-based parallel processing system that provides a wide range of functionalities such as graph processing, machine learning, stream processing, and SQL. This book aims to take your knowledge of Spark to the next level by teaching you how to expand Spark's functionality and implement your data flows and machine/deep learning programs on top of the platform.

The book commences with an overview of the Spark ecosystem. It will introduce you to Project Tungsten and Catalyst, two of the major advancements of Apache Spark 2.x.

You will understand how memory management and binary processing, cache-aware computation, and code generation are used to speed things up dramatically. The book extends to show how to incorporate H20, SystemML, and Deeplearning4j for machine learning, and Jupyter Notebooks and Kubernetes/Docker for cloud-based Spark. During the course of the book, you will learn about the latest enhancements to Apache Spark 2.x, such as interactive querying of live data and unifying DataFrames and Datasets.

You will also learn about the updates on the APIs and how DataFrames and Datasets affect SQL, machine learning, graph processing, and streaming. You will learn to use Spark as a big data operating system, understand how to implement advanced analytics on the new APIs, and explore how easy it is to use Spark in day-to-day tasks.

Style and approach

This book is an extensive guide to Apache Spark modules and tools and shows how Spark's functionality can be extended for real-time processing and storage with worked examples.

Osoby które kupowały "Mastering Apache Spark 2.x - Second Edition", wybierały także:

• Excel 2013. Kurs video. Poziom drugi. Przetwarzanie i analiza danych 79,00 zÅ‚, (35,55 zÅ‚ -55%)
• Zrozumieć BPMN. Modelowanie procesów biznesowych. Wydanie 2 rozszerzone 39,90 zÅ‚, (19,95 zÅ‚ -50%)
• Excel 2016 PL. Biblia 109,00 zÅ‚, (54,50 zÅ‚ -50%)
• Naczelny Algorytm. Jak jego odkrycie zmieni nasz Å›wiat 49,00 zÅ‚, (24,50 zÅ‚ -50%)
• Big Data. Najlepsze praktyki budowy skalowalnych systemów obsÅ‚ugi danych w czasie rzeczywistym 89,00 zÅ‚, (44,50 zÅ‚ -50%)

Spis treści

Mastering Apache Spark 2.x. Advanced techniques in complex Big Data processing, streaming analytics and machine learning - Second Edition eBook -- spis treści

• 1. A first taste and what’s new in ApacheSpark V2
• 2. Apache Spark SQL
• 3. The Catalyst Optimizer
• 4. Project Tungsten
• 5. Apache Spark Streaming
• 6. Structured Streaming
• 7. Apache Spark MLlib
• 8. Apache SparkML
• 9. Apache SystemML
• 10. DeepLearning on Apache Spark with DeepLearning4J, ApacheSystemML,H2O
• 11. Apache Spark GraphX
• 12. ApacheSpark GraphFrames
• 13. ApacheSpark with Jupyter Notebooks on IBM DataScience Experience
• 14. ApacheSpark on Kubernetes