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. It will cover IBM BigInsights as a platform for managing and gaining insights from your big data. As such, you will see how the BigInsights have aligned their offerings to better suit your 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, you will get an introduction to the BigInsights value-add including Big SQL, BigSheets, and Big R.

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. 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. The current ecosystem is challenged and slowed by fragmented and duplicated efforts between different groups. The ODP Core will take the guesswork out of the process and accelerate many use cases by running on a common platform. It allows enterprises to focus on building business driven applications.

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.

OBJECTIVE

IBM BigInsights Description

DW6A1

• 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

DW6B1

• 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.

AUDIENCE

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.

TOPICS

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 and 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

1. (DW6A1)
   • 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
2. (DW6B1)
   • 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.