Mastering Apache Spark 2.x - Second Edition:
bAdvanced analytics on your Big Data with latest Apache Spark 2.x/bh2About This Book/h2ulliAn advanced guide with a combination of instructions and practical examples to extend the most up-to date Spark functionalities./liliExtend your data processing capabilities to process huge chunk of data in mi...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham
Packt Publishing Limited
2017
|
| Ausgabe: | 2 |
| Schlagworte: | |
| Zusammenfassung: | bAdvanced analytics on your Big Data with latest Apache Spark 2.x/bh2About This Book/h2ulliAn advanced guide with a combination of instructions and practical examples to extend the most up-to date Spark functionalities./liliExtend your data processing capabilities to process huge chunk of data in minimum time using advanced concepts in Spark./liliMaster the art of real-time processing with the help of Apache Spark 2.x/li/ulh2Who This Book Is For/h2If 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.h2What You Will Learn/h2ulliExamine Advanced Machine Learning and DeepLearning with MLlib, SparkML, SystemML, H2O and DeepLearning4J/liliStudy highly optimised unified batch and real-time data processing using SparkSQL and Structured Streaming/liliEvaluate large-scale Graph Processing and Analysis using GraphX and GraphFrames/liliApply Apache Spark in Elastic deployments using Jupyter and Zeppelin Notebooks, Docker, Kubernetes and the IBM Cloud/liliUnderstand internal details of cost based optimizers used in Catalyst, SystemML and GraphFrames/liliLearn how specific parameter settings affect overall performance of an Apache Spark cluster/liliLeverage Scala, R and python for your data science projects/li/ulh2In Detail/h2Apache 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. |
| Beschreibung: | 1 Online-Ressource (354 Seiten) |
| ISBN: | 9781785285226 |
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| 520 | |a bAdvanced analytics on your Big Data with latest Apache Spark 2.x/bh2About This Book/h2ulliAn advanced guide with a combination of instructions and practical examples to extend the most up-to date Spark functionalities./liliExtend your data processing capabilities to process huge chunk of data in minimum time using advanced concepts in Spark./liliMaster the art of real-time processing with the help of Apache Spark 2.x/li/ulh2Who This Book Is For/h2If 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. | ||
| 520 | |a Reasonable knowledge of Scala is expected.h2What You Will Learn/h2ulliExamine Advanced Machine Learning and DeepLearning with MLlib, SparkML, SystemML, H2O and DeepLearning4J/liliStudy highly optimised unified batch and real-time data processing using SparkSQL and Structured Streaming/liliEvaluate large-scale Graph Processing and Analysis using GraphX and GraphFrames/liliApply Apache Spark in Elastic deployments using Jupyter and Zeppelin Notebooks, Docker, Kubernetes and the IBM Cloud/liliUnderstand internal details of cost based optimizers used in Catalyst, SystemML and GraphFrames/liliLearn how specific parameter settings affect overall performance of an Apache Spark cluster/liliLeverage Scala, R and python for your data science projects/li/ulh2In Detail/h2Apache 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. | ||
| 520 | |a 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. | ||
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| illustrated | Not Illustrated |
| index_date | 2024-07-03T16:13:34Z |
| indexdate | 2024-07-10T09:01:44Z |
| institution | BVB |
| isbn | 9781785285226 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032477240 |
| oclc_num | 1227483894 |
| open_access_boolean | |
| physical | 1 Online-Ressource (354 Seiten) |
| psigel | ZDB-5-WPSE |
| publishDate | 2017 |
| publishDateSearch | 2017 |
| publishDateSort | 2017 |
| publisher | Packt Publishing Limited |
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| spelling | Kienzler, Romeo Verfasser aut Mastering Apache Spark 2.x - Second Edition Kienzler, Romeo 2 Birmingham Packt Publishing Limited 2017 1 Online-Ressource (354 Seiten) txt rdacontent c rdamedia cr rdacarrier bAdvanced analytics on your Big Data with latest Apache Spark 2.x/bh2About This Book/h2ulliAn advanced guide with a combination of instructions and practical examples to extend the most up-to date Spark functionalities./liliExtend your data processing capabilities to process huge chunk of data in minimum time using advanced concepts in Spark./liliMaster the art of real-time processing with the help of Apache Spark 2.x/li/ulh2Who This Book Is For/h2If 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.h2What You Will Learn/h2ulliExamine Advanced Machine Learning and DeepLearning with MLlib, SparkML, SystemML, H2O and DeepLearning4J/liliStudy highly optimised unified batch and real-time data processing using SparkSQL and Structured Streaming/liliEvaluate large-scale Graph Processing and Analysis using GraphX and GraphFrames/liliApply Apache Spark in Elastic deployments using Jupyter and Zeppelin Notebooks, Docker, Kubernetes and the IBM Cloud/liliUnderstand internal details of cost based optimizers used in Catalyst, SystemML and GraphFrames/liliLearn how specific parameter settings affect overall performance of an Apache Spark cluster/liliLeverage Scala, R and python for your data science projects/li/ulh2In Detail/h2Apache 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. COMPUTERS / Data Modeling & Design COMPUTERS / Programming Languages / Java |
| spellingShingle | Kienzler, Romeo Mastering Apache Spark 2.x - Second Edition COMPUTERS / Data Modeling & Design COMPUTERS / Programming Languages / Java |
| title | Mastering Apache Spark 2.x - Second Edition |
| title_auth | Mastering Apache Spark 2.x - Second Edition |
| title_exact_search | Mastering Apache Spark 2.x - Second Edition |
| title_exact_search_txtP | Mastering Apache Spark 2.x - Second Edition |
| title_full | Mastering Apache Spark 2.x - Second Edition Kienzler, Romeo |
| title_fullStr | Mastering Apache Spark 2.x - Second Edition Kienzler, Romeo |
| title_full_unstemmed | Mastering Apache Spark 2.x - Second Edition Kienzler, Romeo |
| title_short | Mastering Apache Spark 2.x - Second Edition |
| title_sort | mastering apache spark 2 x second edition |
| topic | COMPUTERS / Data Modeling & Design COMPUTERS / Programming Languages / Java |
| topic_facet | COMPUTERS / Data Modeling & Design COMPUTERS / Programming Languages / Java |
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