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Big Data

BIG DATA HADOOP TRAINING

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London IT Training Big Data

Big Data is a term that means a huge amount of data and Hadoop is an open-source framework from Apache that is used to run applications on the cluster. Without Big Data Hadoop training, you may learn very little about Big Data processing.

From this training, you will learn how Big Data is integrated with Hadoop. You will also learn the basic of Hadoop, Introduction of Hadoop Stack and HDFS (Hadoop Distributed File System) and how to use Map and Reduce process using Hadoop. After these basic techniques, this training program will take you to the topics like Scoop, PIG, YARN, Impala and Apache Hive technologies of Hadoop platform. You will also be able to learn design a real-world system using Hadoop platform, as well.

Prerequisites

  • You must know the fundamentals of SQL and Database.
  • You should have a basic understanding of the Data Storage Concept.
  • You should know about the HDFS (Hadoop Distributed File System).
  • You must have a clear understanding of Data Warehouse and cloud storage infrastructure
  • It is required that you have fluency in at least one or more high-level programming languages like Python, Hive, etc.

What will you gain after this course

  • You will be considered as a professional data Science expert
  • You can shape the future of the business of the organization through predictive data analysis
  • You can wind up your job opportunities through these jobs.

Jobs you can get
with a Hadoop Certification

  • Data Science Specialist
  • Data Science Expert
  • Data Scientist
  • Data Analyst
  • Machine learning Engineer

Corporate Group Training

  • Customized Training
  • Onsite / Virtual
  • Instructor-led Delivery
  • For small to large groups

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Get a 10% discount

If you enrol two months in advance

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Hadoop Architecture and HDFS
<ul>
<li>What is Hadoop?</li>
<li>Hadoop History</li>
<li>Distributing Processing System</li>
<li>Core Components of Hadoop</li>
<li>HDFS Architecture</li>
<li>Hadoop Master-Slave Architecture</li>
<li>Daemon types -Name node, Data node, Secondary Name node.</li>
</ul>
Hadoop MapReduce Framework
<ul>
<li>Overview of MapReduce Framework</li>
<li>MapReduce Architecture</li>
<li>Learn about Job tracker and Task tracker</li>
<li>Use cases of MapReduce</li>
<li>Anatomy of MapReduce Program</li>
</ul>
Hive and HiveQL
<ul>
<li>What is Hive?</li>
<li>Hive vs MapReduce</li>
<li>Hive DDL - Create/Show/Drop Tables</li>
<li>Internal and External Tables</li>
<li>Hive DML – Load Files & Insert Data</li>
<li>Hive Architecture & Components</li>
<li>Difference between Hive and RDBMS</li>
<li>Partitions in Hive</li>
</ul>
PIG, Apache SQOOP, and Flume
<ul>
<li> PIG vs Map</li>
<li> Reduce PIG Architecture & Data types</li>
<li> Shell and Utility components</li>
<li> PIG Latin Relational Operators</li>
<li> PIG Latin: File Loaders and UDF</li>
<li> Programming structure in UDF</li>
<li> PIG Jars Import, limitations of PIG</li>
<li> Why and what is SQOOP?</li>
<li> SQOOP Architecture</li>
<li> Benefits of SQOOP</li>
<li> Importing Data Using SQOOP</li>
<li> Apache Flume Introduction</li>
<li> Flume Model and Goals</li>
<li> Features of Flume</li>
<li> Flume Use Case</li>
</ul>

NoSQL Databases, Oozie and Zookeeper
<ul>
<li> What is HBase?</li>
<li> HBase Architecture</li>
<li> HBase Components</li>
<li> Storage Model of HBase</li>
<li> HBase vs RDBMS</li>
<li> Introduction to MongoDB, CRUD</li>
<li> Advantages of MongoDB over RDBMS, Use case</li>
<li> Oozie – Simple/Complex Flow</li>
<li> Oozie Workflow</li>
<li> Oozie Components</li>
<li> Demo on Oozie Workflow in XML</li>
<li> What is Zookeeper</li>
<li> Features of Zookeeper</li>
<li> Zookeeper Data Model</li>
</ul>
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Duration Full time, Part time, Evening and Weekends, Virtual online
Video London, Flexible online
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