Course Outline

1. Introduction to Big Data
   • Academic
   • Early web
   • Web scale
2. Sources (Examples)
   • Internet
   • Transport systems
   • Medical, healthcare
   • Insurance
   • Military and others
3. Hadoop – the free platform for working with big data
   • History
   • Yahoo
   • Platform fragmentation
   • What usage looks like in the enterprise
4. The concepts
   • Load data how you find it
   • Process it when you can
   • Project it into various schemas on the fly
   • Push it back to where you need it
5. The basics
   • What it’s good for
   • What can’t it do / disadvantages
   • Most common use cases for big data
6. Introduction to HDFS
   • Robustness
   • Data Replication
   • Gotchas
7. MapReduce – the core big data function
   • Map explained
   • Sort and shuffle explained
   • Reduce explained
8. YARN
   • How it fits
   • How it works
   • Resource Manager
   • Application Master
9. PIG
   • What it is
   • How it works
   • Compatibilities
   • Advantages
   • Disadvantages
10. Processing Data
    • The Piggy Bank
    • Loading and Illustrating the data
    • Writing a Query
    • Storing the Result
11. HIVE
    • Data warehousing
    • What it is, what it’s not
    • Language compatibilities
    • Advantages
12. OOZIE
    • What it is
    • Complex workflow environments
    • Reducing time-to-market
    • Frequency execution
    • How it works with other big data tools
13. FLUME – stream, collect, store and analyze high-volume log data
    • How it works: Event, source, sink, channel, agent and client
    • How it works illustrated
    • How it works demonstrated
14. SPARK
    • Move over 2012 Big Data tools: Apache SPARK is the new power tool
    • The new open source cluster framework
    • When SPARK performs 100 times faster
    • Performance comparison of Spark and Hadoop
    • What else can it do?
15. HBASE
    • What it is
    • Common use cases
16. Using External Tools

Target Audience

• Software Developer
• Machine Learning Engineer
• Data Scientist
• Business Intelligence Developer
• Research Scientist
• Data Engineer
• Programmer
• Project Manager

What You'll Learn

• Navigate the technology stacks and tools used to work with big data
• Establish a common vocabulary on your teams for applying big data practices
• Get an overview of how big data technologies work: Apache Hadoop, Spark, Pig, Hive, Sqoop, OOZIE, and FLUME
• Design both functional and non-functional requirements for working with big data
• Understand common business cases for big data
• Differentiate between hype and what’s truly possible
• Look at examples of real-world big data use cases
• Select initiatives and projects that have high potential to benefit from big data applications
• Understand what type of staffing, technical skills, and training is required for projects that incorporate or focus on big data

Inclusions

With CCS Learning Academy, you’ll receive:

• Instructor-led training
• Training Seminar Student Handbook
• Pre and Post assessments/evaluations
• Collaboration with classmates (not currently available for self-paced course)
• Real-world learning activities and scenarios
• Exam scheduling support*
• Enjoy job placement assistance for the first 12 months after course completion.
• This course is eligible for CCS Learning Academy’s Learn and Earn Program: get a tuition fee refund of up to 50% if you are placed in a job through CCS Global Tech’s Placement Division*
• Government and Private pricing available.*

*For more details call: 858-208-4141 or email: training@ccslearningacademy.com; sales@ccslearningacademy.com