Synopses & Reviews
Synopsis
This book is a practical guide with easy-to-follow examples and case studies on how to supplant or augment traditional enterprise data warehouse and business intelligence environments with modern big data platforms and next-generation third-party applications. The book revolves around Apache Kudu, Impala, and Spark as the triumvirate comprising the modern big data warehouse platform for large-scale data processing and analysis.
Next-Generation Big Data takes a holistic approach and covers all aspects of the modern enterprise big data environment, treating each as integrated parts the whole ecosystem. It covers not just the native Hadoop technology stack (Apache Kudu, Spark, Impala, HDFS, etc), but also the next-generation third-party tools and applications for real-time and batch data ingestion and processing (StreamSets, NIFI, Talend, Pentaho, Cask), data visualization (Zoomdata, Tableau, Qlik, etc.), data governance and metadata management (Cloudera Navigator, Informatica Metadata Manager, etc.), data wrangling (Datameer, Trifacta, Alytrix, AtScale, etc.), cloud (Cloudera Navigator on AWS and Azure) and data analysis (Data Science Workbench, Jupyter, Zeppelin). It also covers integration with other data platforms (Oracle, SQL Server, MySQL, HBase, Solr, S3).
Finally, the book has an extensive and detailed coverage of big data use cases and interesting real-world case studies from AMD, British Telecom, Cerner, Mastercard, and other Cloudera customers.
What you'll learn
How to deploy Apache Kudu, Impala, and Spark as a cost-effective alternative or "optimization" to enterprise data warehouse environments
An understanding of infrastructure optimization such as EDW/data mart/reporting db/archive db replacements, data archiving, data consolidation and ETL offloading to take advantage of millions of dollars in cost savings
New Cloudera features such as Data Science Workbench, Cloudera Altus, Cloudera Navigator, and Cloudera Navigator Optimizer
Big data "enterprise" topics such as data governance, meta data management, cloud, in-memory computing, use cases, case studies, etc.
Who This Book Is For
Business intelligence and data warehouse (BI/DW) architects, big data engineers, data analysts, ETL developers, IT project managers, database/big data managers, consultants, etc. who are making the jump to big data. Also, seasoned big data professionals who needs to update their knowledge, IT professionals without or little Hadoop experience, and CIO/CTO, VP of data engineering, IT/big data managers/directors.
Synopsis
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
Who This Book Is For Business intelligence and data warehouse professionals who are interested in gaining practical and real-world insight into next-generation big data processing and analytics using Apache Kudu, Impala, and Spark. Experienced big data professionals who would like to learn more about Kudu and other advanced enterprise topics such as real-time data ingestion and complex event processing, Internet of Things, distributed in-memory big data computing, big data cloud deployments, big data governance, metadata management, real-time data visualization, data wrangling, data warehouse optimization and big data warehousing will also benefit from this book.
W
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