IBM BigInsights Foundation






IBM BigInsights Overview


  • 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 your big data needs.

IBM Open Platform (IOP) with Apache Hadoop


  • 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 t 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.
  • Explore advanced concepts, including Oozie and Solr

This intermediate training course is for those who want a foundation of IBM BigInsights. This includes:

  • Big data engineers
  • Data scientist
  • Developers or programmers
  • Administrators who are interested in learning about IBM's Open Platform with Apache Hadoop.

This course consists of two separate modules. The first module is IBM BigInsights Overview and it will give you an overview of IBM's big data strategy as well as a why it is important to understand and use big data. The second module is 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.

There are no pre-requisites for this course but knowledge of Linux would be beneficial.


  • Unit 1: Introduction to Big Data
  • Exercise 1: Setting up the lab environment
  • Unit 2: Introduction to IBM BigInsights
  • Exercise 2: Getting started with IBM BigInsights
  • Unit 3: IBM BigInsights for Analysts
  • Exercise 3: Working with Big SQL and BigSheets
  • Unit 4: IBM BigInsights for Data Scientist
  • Exercise 4: Analyzing data with Big R, Jaql, and AQL
  • Unit 5: IBM BigInsights for Enterprise Management


  • Unit 1: IBM Open Platform with Apache Hadoop
  • Exercise 1: Exploring the HDFS
  • Unit 2: Apache Ambari
  • Exercise 2: Managing Hadoop clusters with Apache Ambari
  • Unit 3: Hadoop Distributed File System
  • Exercise 3:  File access & basic commands with HDFS
  • Unit 4: MapReduce and Yarn
  • Topic 1:  Introduction to MapReduce based on MR1
  • Topic 2:  Limitations of MR1
  • Topic 3:  YARN and MR2
  • Exercise 4: Creating and coding a simple MapReduce job (Possibly a more complex second Exercise)
  • Unit 5: Apache Spark
  • Exercise 5: Working with Spark's RDD to a Spark job
  • Unit 6: Coordination, management, and governance
  • Exercise 6: Apache ZooKeeper, Apache Slider, Apache Knox
  • Unit 7: Data Movement
  • Exercise 7: Moving data into Hadoop with Flume and Sqoop
  • Unit 8: Storing and Accessing Data
  • Topic 1:  Representing Data:  CSV, XML, JSON, and YAML
  • Topic 2:  Open Source Programming Languages: Pig, Hive, and Other [R, Python, etc]
  • Topic 3:  NoSQL Concepts
  • Topic 4:  Accessing Hadoop data using Hive
  • Exercise 8: Performing CRUD operations using the HBase shell
  • Topic 5:  Querying Hadoop data using Hive
  • Exercise 9:  Using Hive to Access Hadoop / HBase Data
  • Unit 9: Advanced Topics
  • Topic 1: Controlling job workflows with Oozie
  • Topic 2: Search using Apache Solr
  • No lab exercises

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