Curso de Dw606g: Ibm Open Platform with Apache Hadoop Online

Información
itFormacion On-Line
Online
Preguntas Frecuentes
Requerimientos

None, however, knowledge of Linux would be beneficial.
Temario
Objetivos

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


Dirigido a

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 Hado


Temario

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
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