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This training course is for those who want a foundation of IBM BigInsights and consists of two separate modules. The first module is an IBM BigInsights overview and gives participants an overview of IBM's big data strategy as well as a why it is important to understand and use big data. It will cover IBM BigInsights as a platform for managing and gaining insights from big data. As such, participants will see how the BigInsights have aligned offerings to better suit their needs with the IBM Open Platform (IOP) along with the three specialized modules with value-add that sits on top of the IOP. Along with that, participants will get an introduction to the BigInsights value-add including Big SQL, BigSheets, and Big R. The second module is over the IBM Open Platform with Apache Hadoop. IBM Open Platform (IOP) with Apache Hadoop is the first premiere collaborative platform to enable Big Data solutions to be developed on the common set of Apache Hadoop technologies. The Open Data Platform initiative (ODP) is a shared industry effort focused on promoting and advancing the state of Apache Hadoop and Big Data technologies for the enterprise. This module provides an in-depth introduction to the main components of the ODP core --namely Apache Hadoop (inclusive of HDFS, YARN, and MapReduce) and Apache Ambari -- as well as providing a treatment of the main open-source components that are generally made available with the ODP core in a production Hadoop cluster.

Objectives:

 Understand the purpose of big data and know why it is important;

 List the sources of data (data-at-rest vs data-in-motion);

 Describe the IBM BigInsights offering;

 Utilize the various IBM BigInsights tools including Big SQL, BigSheets, Big R, Jaql and AQL for big data needs;

 List and describe the major components of the open-source Apache Hadoop stack and the approach taken by the Open Data Foundation;

 Manage and monitor Hadoop clusters with Apache Ambari and related components;

 Explore the Hadoop Distributed File System (HDFS) by running Hadoop commands;

 Understand the differences between Hadoop 1 (with MapReduce 1) and Hadoop 2 (with YARN and MapReduce 2);

 Create and run basic MapReduce jobs using command line;

 Explain how Spark integrates int the Hadoop ecosystem;

 Execute iterative algorithms using Spark's RDD;

 Explain the role of coordination, management, and governance in the Hadoop ecosystem using Apache Zookeeper, Apache Slider, and Apache Knox;

 Explore common methods for performing data movement;

 Configure Flume for data loading of log files;

 Move data int the HDFS from relational databases using Sqoop;

 Understand when to use various data storage formats (flat files, CSV/delimited, Avro/Sequence files, Parquet, etc.);

 Review the differences between the available open-source programming languages typically used with Hadoop (Pig, Hive) and for Data Science (Python, R);

 Query data from Hive;

 Perform random access on data stored in HBase; and

 Explore advanced concepts, including Oozie and Solr.