Details
Practical Apache Spark
Using the Scala API|
62,99 € |
|
| Verlag: | Apress |
| Format: | |
| Veröffentl.: | 12.12.2018 |
| ISBN/EAN: | 9781484236529 |
| Sprache: | englisch |
Dieses eBook enthält ein Wasserzeichen.
Beschreibungen
<div><div>Work with Apache Spark using Scala to deploy and set up single-node, multi-node, and high-availability clusters. This book discusses various components of Spark such as Spark Core, DataFrames, Datasets and SQL, Spark Streaming, Spark MLib, and R on Spark with the help of practical code snippets for each topic. <i>Practical Apache Spark</i> also covers the integration of Apache Spark with Kafka with examples. You’ll follow a learn-to-do-by-yourself approach to learning – learn the concepts, practice the code snippets in Scala, and complete the assignments given to get an overall exposure. </div><br></div>On completion, you’ll have knowledge of the functional programming aspects of Scala, and hands-on expertise in various Spark components. You’ll also become familiar with machine learning algorithms with real-time usage.<div><br></div><div><b>What You Will Learn</b></div><div><ul><li>Discover the functional programming features of Scala<br></li><li>Understand the completearchitecture of Spark and its components</li><li>Integrate Apache Spark with Hive and Kafka <br></li><li>Use Spark SQL, DataFrames, and Datasets to process data using traditional SQL queries<br></li><li>Work with different machine learning concepts and libraries using Spark's MLlib packages<br></li></ul></div><div><br></div><div><b>Who This Book Is For</b></div><div><br></div><div>Developers and professionals who deal with batch and stream data processing. </div><div><br><br></div>
<div><p>1. Scala - Functional Programming Aspects. - 2. Single & Multi-node cluster setup. - 3. Introduction to Apache Spark and Spark Core. - 4. Spark SQL, Dataframes & Datasets. - 5. Introduction to Spark Streaming. - 6. Spark Structured Streaming. - 7. Spark Streaming with Kafka. - 8. Spark Machine Learning Library. - 9. Working with SparkR. - 10. Spark - Real time use case.</p><br></div>
<p>Subhashini Chellappan is a technology enthusiast with expertise in the big data and cloud space. She has rich experience in both academia and the software industry. Her areas of interest and expertise are centered on business intelligence, big data analytics and cloud computing.<br></p>
<p></p><p></p>
<p>Dharanitharan Ganesan is a senior analyst with five years of experience in IT. He has a high level of exposure and experience in big data – Apache Hadoop, Apache Spark and various Hadoop ecosystem components. He has a proven track record of improving efficiency and productivity through the automation of various routine and administrative functions in business intelligence and big data technologies. His areas of interest and expertise are centered on machine learning algorithms, statistical modelling and predictive analysis.</p><br><br>
<p></p><p></p>
<p>Dharanitharan Ganesan is a senior analyst with five years of experience in IT. He has a high level of exposure and experience in big data – Apache Hadoop, Apache Spark and various Hadoop ecosystem components. He has a proven track record of improving efficiency and productivity through the automation of various routine and administrative functions in business intelligence and big data technologies. His areas of interest and expertise are centered on machine learning algorithms, statistical modelling and predictive analysis.</p><br><br>
<div><div><div>Work with Apache Spark using Scala to deploy and set up single-node, multi-node, and high-availability clusters. This book discusses various components of Spark such as Spark Core, DataFrames, Datasets and SQL, Spark Streaming, Spark MLib, and R on Spark with the help of practical code snippets for each topic. <i>Practical Apache Spark</i> also covers the integration of Apache Spark with Kafka with examples. You’ll follow a learn-to-do-by-yourself approach to learning – learn the concepts, practice the code snippets in Scala, and complete the assignments given to get an overall exposure. </div><br></div><div>On completion, you’ll have knowledge of the functional programming aspects of Scala, and hands-on expertise in various Spark components. You’ll also become familiar with machine learning algorithms with real-time usage.</div></div><div><br></div><div>You will:</div><div><ul><li>Discover the functional programming features of Scala<br></li><li>Understand the complete architecture of Spark and its components</li><li>Integrate Apache Spark with Hive and Kafka <br></li><li>Use Spark SQL, DataFrames, and Datasets to process data using traditional SQL queries<br></li><li>Work with different machine learning concepts and libraries using Spark's MLlib packages</li></ul></div>
Contains extensive coverage of machine-learning algorithms with real-time code implementation using Spark MLib Explains the SparkR real-time module with code implementation Covers Spark Streaming and Spark Integration examples with other big data components such as Kafka
Diese Produkte könnten Sie auch interessieren:
