Big Data Overview

See Course Outline

Training for your Team

1
Days
  • Private Class for your Team
  • Online or On-location
  • Customizable
  • Expert Instructors

Training for Yourself

$625.00
or 1 vouchers
  • Live Online Training
  • For Individuals
  • Expert Instructors
  • Guaranteed to Run
  • 100% Free Re-take Option
  • 1-minute Video
Overview

We all know - Big Data is here in a Big way. However, processing that data can still be a Big challenge. This Big Data Overview provides an in-depth overview of the choices you have in processing Big Data. It provides an introduction to what Big Data is, the types of data you might have, approaches to working on and processing the data, and the capabilities, strengths, and weaknesses of those approaches.

After taking this course, you will have a clear understanding of what Big Data is and of the various types of data you may encounter. You will know different techniques and technologies for working with your data, where those technologies are a good fit, and where they are not. You will be well prepared for evaluating what approach is best suited for your needs.

Goals
  1. Understand what Big Data is
  2. Know the difference between "data-at-rest" and "data-in-motion"
  3. Understand what map-reduce / Hadoop is, and what it can do
  4. Be aware of query technologies for easily querying with Hadoop (e.g. Hive, Pig, and others)
  5. Understand what NoSQL databases are and what they can do
  6. Become familiar with the choices in the NoSQL landscape
  7. Understand the strengths and weaknesses of different NoSQL technologies
  8. Be well-informed on your choices in Big Data processing, and evaluate them for your needs
Outline
  1. Understanding Big Data
    1. Big Data Characteristics
    2. Relational Model Overview
    3. Working with Big Data
    4. Data Consistency and CAP
  2. NewSQL Databases
    1. NewSQL Overview
    2. Product Overviews
    3. Summary
  3. NoSQL Overview
    1. Differences from Relational Model
    2. Types of NoSQL Stores
    3. Document Data Model
    4. Graph Data Model
    5. Key/Value
    6. Wide Columnar
    7. Hadoop
  4. Hadoop and MapReduce
    1. Overview
    2. HDFS
    3. YARN
    4. MapReduce
    5. Summary
  5. Other Processing Technologies
    1. Apache Pig and Hive
    2. Apache Impala
    3. Apache Storm
    4. Apache Spark
    5. Session 6: MongoDB
    6. Overview and Architecture
    7. Summary of Strenghts/Weaknesses
  6. Cassandra Database
    1. Overview and Architecture
    2. Summary of Strenghts/Weaknesses
    3. Session 8: Other Databases and Tools
    4. HBase
    5. Neo4j
Class Materials

Each student in our Live Online and our Onsite classes receives a comprehensive set of materials, including course notes and all the class examples.

Class Prerequisites

Experience in the following would be useful for this Hadoop class:

  • Some knowledge of databases and data processing is useful, but not required.
Preparing for Class

No cancelation for low enrollment

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