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HADOOP / Bigdata

Big data is a term that describes the substantial volume of information – both Structured and unstructured – that immerses a business on an everyday premise. Read More

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HADOOP / Bigdata

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  • Up Coming Demos
  • Description
  • Curriculum

Up Coming Demos

14

Apr

Sat - Sun (5 Weeks)
7:00AM - 7:00AM (IST)

Enroll

14

Apr

Sat - Sun (5 Weeks)
7:00AM - 7:00AM (IST)

Enroll

14

Apr

Sat - Sun (5 Weeks)
7:00AM - 7:00AM (IST)

Enroll

HADOOP ONLINE TRAINING

What is Big data?

Big data is a term that describes the substantial volume of information – both Structured and unstructured – that immerses a business on an everyday premise. Yet, it’s not the measure of information that is essential. It’s what associations do with the information that issues. Huge information can be investigated for experiences that prompt better choices and key business moves.

What is Hadoop?

Hadoop is an open source, Java-based programming structure that backings the preparing and capacity of greatly expansive informational indexes in an appropriated registering condition. It is a piece of the Apache extend supported by the Apache Software Foundation.

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    • Lecture
      Who should take this course?
      • Big Data career opportunities are on the rise, and Hadoop is quickly becoming a must-know technology for the following professionals:

      • 1.Software Developers and Architects
      • 2.Analytics Professionals
      • 3.Senior IT professionals
      • 4.Testing and Mainframe professionals
      • 5.Data Management Professionals
      • 6.Business Intelligence Professionals
      • 7.Project Managers
      • 8.Aspiring Data Scientists
      • 9.Graduates looking to build a career in Big Data Analytics

      • Prerequisite:
      • As the knowledge of Java is necessary for this course, we are providing a complimentary access to “Java Essentials for Hadoop” course For Spark we use Python and Scala and an Ebook has been provided to help you with the sameKnowledge of an operating system like Linux is useful for the course.
      25 hour(s)
    • Lecture
      Big Data History and Current Considerations
      • While the term “big data” is relatively new, the act of gathering and storing large amounts of information for eventual analysis is ages old. The concept gained momentum in the early 2000s when industry analyst Doug Laney articulated the now-mainstream definition of big data as the three Vs:
      • Volume:
      • Organizations collect data from a variety of sources, including business transactions, social media and information from sensor or machine-to-machine data. In the past, storing it would’ve been a problem – but new technologies (such as Hadoop) have eased the burden.
      • Velocity:
      • Data streams in at an unprecedented speed and must be dealt with in a timely manner. RFID tags, sensors and smart metering are driving the need to deal with torrents of data in near-real time.
      • Variety:
      • Data comes in all types of formats – from structured, numeric data in traditional databases to unstructured text documents, email, video, audio, stock ticker data and financial transactions.
      • At SAS, we consider two additional dimensions when it comes to big data:
      • Variability:
      • In addition to the increasing velocities and varieties of data, data flows can be highly inconsistent with periodic peaks. Is something trending in social media? Daily, seasonal and event-triggered peak data loads can be challenging to manage. Even more so with unstructured data.
      • Complexity:
      • Today’s data comes from multiple sources, which makes it difficult to link, match, cleanse and transform data across systems. However, it’s necessary to connect and correlate relationships, hierarchies and multiple data linkages or your data can quickly spiral out of control.
      25 hour(s)
    • Lecture
      Course Objectives
      • 1.Explain the need for Big Data, and list its applications.
      • 2.Demonstrate the mastery of HDFS concepts and MapReduce framework
      • 3.Use Sqoop and Flume to load data into Hadoop File System
      • 4.Run queries using Pig and Hive
      • 5.Install and configure HBase
      • 6.Discuss and differentiate various commercial distributions of Big Data like Cloudera and Hortonworks.
      25 hour(s)
    • Lecture
      Hadoop Course COURSE CURRICULUM
      • -->Hadoop Developer/Admin Training Course Content
      • -->Hadoop Architecture
      • 1.Introduction to Hadoop
      • 2.Parallel Computer vs. Distributed Computing
      • 3.How to install Hadoop on your system
      • 4.How to install Hadoop cluster on multiple machines
      • Hadoop Daemons introduction:
      • 1.NameNode, DataNode, JobTracker, TaskTracker
      • 2.Exploring HDFS (Hadoop Distributed File System)
      • 3.Exploring the HDFS Apache Web UI
      • 4.NameNode architecture
      • 5.(EditLog, FsImage, location of replicas)
      • 6.Secondary NameNode architecture
      • DataNode architecture
      • -->MapReduce Architecture
      • 1.Exploring JobTracker/TaskTracker
      • 2.How to run a Map-Reduce job
      • 3.Exploring Mapper/Reducer/Combiner
      • 4.Shuffle: Sort & Partition
      • 5.Input/output formats
      • 6.Exploring the Apache MapReduce Web UI
      • -->Hadoop Developer Tasks
      • 1.Writting a Map-Reduce programme
      • 2.Reading and writing data using Java
      • 3.Hadoop Eclipse integration
      • 4.Mapper in details
      • 5.Reducer in details
      • 6.Using Combiners
      • 7.Reducing Intermediate Data with Combiners
      • 8.Writing Partitioners for Better Load Balancing
      • 9.Sorting in HDFS
      • 10.Searching in HDFS
      • 11.Hands-On Exercise
      • -->HBase Architecture
      • 1.Routine Administrative Procedures
      • 2.Understanding dfsadmin and mradmin
      • 3.Block Scanner, Balancer
      • 4.Health Check & Safe mode
      • 5.Monitoring and Debugging on a production cluster
      • 6.NameNode Back up and Recovery
      • 7.DataNode commissioning/decommissioning
      • 8.ACL (Access control list)
      • 9.Upgrading Hadoop
      • -->Hive Architecture
      • 1.Introduction to Hive
      • 2.HBase vs Hive
      • 3.Installation of Hive on your system
      • 4.HQL (Hive query language )
      • 5.Basic Hive commands
      • 6.Hands-on-Exercise
      • -->PIG Architecture hadoop
      • 1.Introduction to Pig
      • 2.Installation of Pig on your system
      • 3.Basic Pig commands
      • 4.Hands-On Exercise
      • -->Sqoop Architecture
      • 1.Introduction to Sqoop
      • 2.Installation of Sqoop on your system
      • 3.Import/Export data from RDBMS to HDFS
      • 4.Import/Export data from RDBMS to HBase
      • 5.Import/Export data from RDBMS to Hive
      • 6.Hands-On Exercise
      • -->Mini Project / POC ( Proof of Concept )
      • 1.Facebook-Hive POC
      • 2.Usages of Hadoop/Hive @ Facebook
      • 3.Static & dynamic partitioning
      • 4.UDF ( User defined functions )
      25 hour(s)

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