Next-Generation Big Data: A Practical Guide to Apache Kudu, Impala, and Spark - Softcover

Quinto, Butch

Softcover

ISBN 10: 1484231465 ISBN 13: 9781484231463

Publisher: Apress, 2018

Synopsis

Details how to integrate popular third-party applications and platforms such as StreamSets, ZoomData, Talend, Pentaho, Cask, Oracle, and SQL Server. with next-generation big data technologies such as Spark, Kudu, Impala, etc

First book covering Apache Kudu—a game-changer relational data store from Cloudera that will disrupt the traditional data warehouse market

Features big data use cases and case studies from some of the most successful deployments—GoPro,  Mastercard, British Telecom, Navistar, oPower, Cerner, Shopzilla and Caesars Entertainment

About this title

About the Author

Butch Quinto is Director of Analytics and Information Management at Deloitte where he leads technology innovation, strategy, solutions development and delivery, business development, vendor alliance, and due diligence. He is also Technical Leader of Deloitte’s ClearLight Lab, an R&D division that conducts innovative and game-changing research around advanced analytics, artificial intelligence, Internet of things, and big data.

Butch has more than 20 years of experience in various technical and leadership roles in several industries including banking and finance, telecommunications, government, utilities, transportation, e-commerce, retail, technology, manufacturing, and bioinformatics. Butch is a recognized thought leader and a frequent speaker at conferences and events. He is a contributor to the Apache Spark and Apache Kudu open source projects, founder of the Cloudera Melbourne User Group, and Deloitte’s Director of Alliance for Cloudera.

From the Back Cover

Utilize this practical and easy-to-follow guide to modernize traditional enterprise data warehouse and business intelligence environments with next-generation big data technologies.

Next-Generation Big Data takes a holistic approach, covering the most important aspects of modern enterprise big data. The book covers not only the main technology stack but also the next-generation tools and applications used for big data warehousing, data warehouse optimization, real-time and batch data ingestion and processing, real-time data visualization, big data governance, data wrangling, big data cloud deployments, and distributed in-memory big data computing. Finally, the book has an extensive and detailed coverage of big data case studies from Navistar, Cerner, British Telecom, Shopzilla, Thomson Reuters, and Mastercard.

What You’ll Learn:

• Install Apache Kudu, Impala, and Spark to modernize enterprise data warehouse and business intelligence environments, complete with real-world, easy-to-follow examples, and practical advice
  
• Integrate HBase, Solr, Oracle, SQL Server, MySQL, Flume, Kafka, HDFS, and Amazon S3 with Apache Kudu, Impala, and Spark
  
• Use StreamSets, Talend, Pentaho, and CDAP for real-time and batch data ingestion and processing
  
• Utilize Trifacta, Alteryx, and Datameer for data wrangling and interactive data processing
  
• Turbocharge Spark with Alluxio, a distributed in-memory storage platform
  
• Deploy big data in the cloud using Cloudera Director
  
• Perform real-time data visualization and time series analysis using Zoomdata, Apache Kudu, Impala, and Spark
  
• Understand enterprise big data topics such as big data governance, metadata management, data lineage, impact analysis, and policy enforcement, and how to use Cloudera Navigator to perform common data governance tasks
  
• Implement big data use cases such as big data warehousing, data warehouse optimization, Internet of Things, real-time data ingestion and analytics, complex event processing, and scalable predictive modeling
  
• Study real-world big data case studies from innovative companies, including Navistar, Cerner, British Telecom, Shopzilla, Thomson Reuters, and Mastercard

Edition details

• Publisher: Apress
• Publication date: 2018
• Language: English
• ISBN 10: 1484231465
• ISBN 13: 9781484231463
• Binding: Paperback
• Edition number: 1
• Number of pages: 580