APPLIED BIG DATA AND BUSINESS INTELLIGENCE WITH SOFTWARE TOOLS‪.‬

The book begins by looking at massive computing tools in Big Data ecosystems with a focus on Hadoop, Mapreduce, Hadoop Distribute File System, and Hadoop Common Components (Pig, Hive, Flume, Oozie, Hbase, Sqoop, Mahout, and others). Job automation and examples developed with SQL Server are discussed below. Apache Ambari's Hadoop ecosystem is also introduced. Additionally, the SAS Big Data Analytics tools are presented (SAS Access Interface to Hadoop, SAS Data Management, SAS Visual Analytics, SAS Visual Statistics, SAS In Memory Statistics for Hadoop, SAS High Performance Data Mining, SAS High Performance Text Mining, SAS VIYA, etc.) Big Data Analytics tools from Oracle (Big Data Appliance, Big Data Connectors, NoSQL Database, Exadata, Business Analytics, etc.), Microsoft (HDInsight, Azure, etc.) and IBM (IBM Solution for Hadoop Power Systems Edition, IBM AIX Solution Editions for Cognos and SPSS, IBM SPSS Modeler, etc.). The quality and integrity of data in Big Data processes and the movement of data between clusters are addressed below. As an example, the copy and movement of databases between servers in SQL Server is developed. HYPER-V, Hadoop, and Ganglia cluster monitoring tools, as well as web interface and other tools, are covered later. Finally, the techniques of Big Data and Business Intelligence are deepened. The most important Business Intelligence tools (Business Objects, MicroStrategy, Tableau, Power BI, Qlik, Domo, Pentaho, etc.) are analyzed with special attention to dashboards. SAS Visual Analytics tools and SAP tools for dashboards are described. Finally, the implementation of KDD (Knowledge Discovery in Data Bases) with SAS (SAS Enterprise Miner) and IBM (IBM SPSS Modeler) tools is described through examples.