Introduction to Big Data Training

Level: Foundation
RATING: 4.6/5 4.56/5 Based on 718 Reviews

This hands-on Introduction to Big Data training provides a unique approach to help you act on data for real business gain. The focus is not what a tool can do, but what you can do with the output from the tool. Gain the skills you need to store, manage, process, and analyze massive amounts of unstructured data to create an appropriate data lake.

Introduction to Big Data Training

Key Features of this Intro to Big Data Training:

  • Learn Big Data Analytics
  • After-course instructor coaching benefit
  • Learning Tree end-of-course exam included
  • After-course computing sandbox included

You Will Learn How To:

  • Store, manage, and analyze unstructured data
  • Select the correct big data stores for disparate data sets
  • Process large data sets using Hadoop to extract value
  • Query large data sets in near real time with Pig and Hive
  • Plan and implement a big data strategy for your organization

Certifications/Credits:

CPE 17 Credits

Choose the Intro to Big Data Training Solution That Best Fits Your Individual Needs or Organizational Goals

LIVE, INSTRUCTOR-LED

In Class & Live, Online Training

  • 3-day instructor-led training course
  • Earn 17 NASBA credits (live, in-class training only)
  • One-on-one after-course instructor coaching
  • After-course computing sandbox
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Standard $2650

Government $2355

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In Class & Live, Online Training

  • Aug 22 - 24 (3 Days)
    9:00 AM - 4:30 PM EDT
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  • Aug 29 - 31 (3 Days)
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Important Intro to Big Data Course Information

  • Big Data Training Course Description

    In this hands-on Introduction to Big Data Course, learn to leverage big data analysis tools and techniques to foster better business decision-making – before you get into specific products like Hadoop training (just to name one). Learn ways of storing data that allow for efficient processing and analysis, and gain the skills you need to store, manage, process, and analyze massive amounts of unstructured data to create an appropriate data lake.

     

    View this Big Data training course as part of a learning path defined by job roles ›

  • Recommended Experience

    • Working knowledge of the Microsoft Windows platform and basic database concepts
  • Who Should Attend

    • Anyone needing to implement, enhance your big data environment and looking to advance their analytics career by ensuring foundational knowledge
    • Typical job roles include: Project Managers and IT Managers, Database Administrators & Data Architects, Developers & SQL Developers, Data Scientists & Business Intelligence
  • All-Inclusive: After-Course Coaching for Real-World Application

    Learning Tree is with you from the beginning of your planning until you return to your job ready to apply your new skills – with instructor coaching to answer real-world big data implementation challenges.
  • Take Your Big Data Course Online or In-person:

    Schedules are busy, but big data training online makes it easy to level-up your career. If you need Big Data online training, we’ve got you covered. Our AnyWare course delivery option gives you the advantages of a live classroom right from the comfort of your computer screen – no matter where you are.

Intro to Big Data Course Outline

  • Introduction to Big Data

    Defining Big Data

    • The four dimensions of Big Data: volume, velocity, variety, veracity
    • Introducing the Storage, MapReduce and Query Stack

    Delivering business benefit from Big Data

    • Establishing the business importance of Big Data
    • Addressing the challenge of extracting useful data
    • Integrating Big Data with traditional data
  • Storing Big Data

    Analyzing your data characteristics

    • Selecting data sources for analysis
    • Eliminating redundant data
    • Establishing the role of NoSQL

    Overview of Big Data stores

    • Data models: key value, graph, document, column–family
    • Hadoop Distributed File System
    • HBase
    • Hive
    • Cassandra
    • Hypertable
    • Amazon S3
    • BigTable
    • DynamoDB
    • MongoDB
    • Redis
    • Riak
    • Neo4J

    Selecting Big Data stores

    • Choosing the correct data stores based on your data characteristics
    • Moving code to data
    • Implementing polyglot data store solutions
    • Aligning business goals to the appropriate data store
  • Processing Big Data

    Integrating disparate data stores

    • Mapping data to the programming framework
    • Connecting and extracting data from storage
    • Transforming data for processing
    • Subdividing data in preparation for Hadoop MapReduce

    Employing Hadoop MapReduce

    • Creating the components of Hadoop MapReduce jobs
    • Distributing data processing across server farms
    • Executing Hadoop MapReduce jobs
    • Monitoring the progress of job flows

    The building blocks of Hadoop MapReduce

    • Distinguishing Hadoop daemons
    • Investigating the Hadoop Distributed File System
    • Selecting appropriate execution modes: local, pseudo–distributed and fully distributed

    Handling streaming data

    • Comparing real–time processing models
    • Leveraging Storm to extract live events
    • Lightning–fast processing with Spark and Shark
  • Tools and Techniques to Analyze Big Data

    Abstracting Hadoop MapReduce jobs with Pig

    • Communicating with Hadoop in Pig Latin
    • Executing commands using the Grunt Shell
    • Streamlining high–level processing

    Performing ad hoc Big Data querying with Hive

    • Persisting data in the Hive MegaStore
    • Performing queries with HiveQL
    • Investigating Hive file formats

    Creating business value from extracted data

    • Mining data with Mahout
    • Visualizing processed results with reporting tools
    • Querying in real time with Impala
  • Developing a Big Data Strategy

    Defining a Big Data strategy for your organization

    • Establishing your Big Data needs
    • Meeting business goals with timely data
    • Evaluating commercial Big Data tools
    • Managing organizational expectations

    Enabling analytic innovation

    • Focusing on business importance
    • Framing the problem
    • Selecting the correct tools
    • Achieving timely results
  • Implementing a Big Data Solution

    • Selecting suitable vendors and hosting options
    • Balancing costs against business value
    • Keeping ahead of the curve

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