IBM Open Platform with Apache Hadoop (BigInsights V4.0) - SPVC

Arrow ECS Training

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In London

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Description

  • Type

    Course

  • Location

    London

  • Duration

    2 Days

  • Start date

    Different dates available

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.

If you are enrolling in a Self Paced Virtual Classroom or Web Based Training course, before you enroll, please review the Self-Paced Virtual Classes and Web-Based Training Classes on our Terms and Conditions page, as well as the system requirements, to ensure that your system meets the minimum requirements for this course.

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London
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Arrow Ecs Training, 56433

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Different dates availableEnrolment now open

About this course



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 into 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 into 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.
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.

None, however, knowledge of Linux would be beneficial.

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Subjects

  • Web
  • Apache

Course programme

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

See related categories

IBM Open Platform with Apache Hadoop (BigInsights V4.0) - SPVC

Price on request

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