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Big Data Boot Camp - Online

Course Code: STTA BIGDATABC
Length: 2 Days
Tuition: $1,795.00
Official

Schedule for this Course

There are no dates scheduled for this course.
If you would like to be added to the wait list for this class Click Here

Course Description:

PMI: 14 PDUs
NASBA: 12 CPEs

Learn how to harness big data in the real world, in your own situation. This course is a comprehensive introduction to Big Data with an emphasis on Apache Hadoop.

This big data training course will provide a technical overview of Apache Hadoop for project managers, business managers and data analysts. Students will understand the overall big data space, technologies involved and will get a detailed overview of Apache Hadoop. The course will expose students to real world use cases to comprehend the capabilities of Apache Hadoop. Students will also learn about YARN and HDFS and how to develop applications and analyze Big Data stored in Apache Hadoop using Apache Pig and Apache Hive. Each topic will provide hands on experience to the students.

The course is developed and taught by certified Hadoop consultants who have a passion for teaching and help deliver value to various clients using Big Data and Hadoop technologies on a daily basis.

Features of this Class:

  • Led by experienced Big Data and Hadoop consultants
  • Hands on Activities that will immerse you into the capabilities of the Hadoop ecosystem
  • Assumes no prior background in Big data
  • Material will be designed by a computer science professor using pedagogical techniques to help the student understand the material in an easier manner.
  • Provides both foundational and practical knowledge that is essential for successful big data ventures.
  • Fast paced introduction to get you up to speed with Big Data.
  • Includes materials covered in most Hadoop certification exams
  • Labs with practice sessions on a Hadoop cluster
  • Will include insights learned from experience on real projects
  • Will include things to do and things not to do with big data projects

Big data isn't just for the big players. Learn to use it in the real world!
"Big data" is a hot buzzword, but most organizations are struggling to put it to practical use. Without assuming any prior knowledge of Apache Hadoop or big data management, this course teaches a wide range of professional roles how to tap and manage the potential benefits of big data, including:

  • Discovering customer insights buried in your existing data
  • Uncovering product opportunities from data insights
  • Pinpointing decision points and criteria
  • Scaling your existing workflows and operations
  • Learning to ask questions that drive tangible business value from Big Data tools

This big data training course is an interactive, hands-on experience that doesn't require deep technical knowledge. In-depth real-world labs give you practical experience with an actual Hadoop environment and real big data strategies.

20 Immediate Benefits of Participating in this Workshop:

  1. Learn about the big data ecosystem
  2. Understand the benefits and ROI you can get from your existing data
  3. Learn about Hadoop and how it is transforming the workspace
  4. Learn about MapReduce and Hadoop Distributed File system
  5. Learn about using Hadoop to identify new business opportunities
  6. Learn about using Hadoop to improve data management processes
  7. Learn about using Hadoop to clarify results
  8. Learn about using Hadoop to expand your data sources
  9. Learn about scaling your current workflow to handle more users and lower your overall performance cost
  10. Learn about the various technologies that comprise the Hadoop ecosystem
  11. Learn how to write a simple map-reduce job from Java or your favorite programming language
  12. Learn how to use a very simple scripting language to transform your data
  13. Learn how to use a SQL like declarative language to analyze large quantities of data
  14. Learn how to connect your existing data warehouse to the Hadoop ecosystem
  15. Learn how to move your data to the Hadoop ecosystem
  16. Learn how to move the results of your data analysis to Business Intelligence Tools like Tableaux
  17. Learn how to automate your workflow using oozie
  18. Learn about polyglot persistence and identifying the right tool for the right job
  19. Learn about future trends in Big data and technologies to keep an eye on
  20. Discover tips and tricks behind successful Hadoop deployments

Prerequisites

No prior knowledge of big data and/or Hadoop is required for this class. Some prior programming experience is a plus for this class, but not necessary.

Course Outline:

1. Introduction to Big Data

  • Big Data - beyond the obvious trends
    • Technologies involved
    • Business drivers
    • Implications for enterprise computing
  • Exponentially increasing data
    • ERP Data
    • CRM Data
    • Web Data
    • Big Data
  • Big data sources
    • Sensors
    • Social
    • Geospatial
    • Video
    • Machine to machine
    • Others
  • Data warehousing, business intelligence, analytics, predictive statistics, data science

2. Survey of Big Data technologies

  • First generation systems
    • RDBMS systems
    • ETL systems
    • BI systems
  • Second generation systems
    • Columnar databases with compression
    • MPP architectures
    • Data warehousing appliances
  • Enterprise search
  • Visualizing and understanding data with processing
    • Streaming processing
    • Statistical processing
    • Data visualization
  • NOSQL databases
    • How do technologies like mongodb, MarkLogic and couchdb fit in?
    • What is polyglot persistence?
  • Apache Hadoop

3. Introduction to Hadoop

  • What is Hadoop? Who are the major vendors?
  • A dive into the Hadoop Ecosystem
  • Benefits of using Hadoop
  • How to use Hadoop within your infrastructure?
    • Where do we use Hadoop?
    • Where do we look at options besides Hadoop?

4. Introduction to MapReduce

  • What is MapReduce?
  • Why do you need MapReduce?
  • Using Mapreduce with Java and Ruby

Lab: How to use MapReduce in Hadoop?

5. Introduction to Yarn

  • What is Yarn?
  • What are the advantages of using Yarn over classical MapReduce?
  • Using Yarn with Java and Ruby

Lab: How to use Yarn within Hadoop?

6. Introduction to HDFS

  • What is HDFS?
  • Why do you need a distributed file system?
  • How is a distributed file system different from a traditional file system?
  • What is unique about HDFS when compared to other file systems?
  • HDFS and reliability?
  • Does it offer support for compressions, checksums and data integrity?

Lab: Overview of HDFS commands

7. Data Transformation

  • Why do you need to transform data?
  • What is Pig?
  • Use cases for Pig

Lab: Hands on activities with Pig

8. Structured Data Analysis?

  • How do you handle structured data with Hadoop?
  • What is Hive/HCatalog?
  • Use cases for Hive/HCatalog

Lab: Hands on activities with Hive/HCatalog

9. Loading data into Hadoop

  • How do you move your existing data into Hadoop?
  • What is Sqoop?

Lab: Hands on activities with Sqoop

10. Automating workflows in Hadoop

  • Benefits of Automation
  • What is oozie?
  • Automatically running workflows
  • Setting up workflow triggers

Lab: Demonstration of oozie

11. Exploring opportunities in your own organization

  • Framing scenarios
  • Understanding how to ask questions
  • Tying possibilities to your own business drivers
  • Common opportunities
  • Real world examples

Hands-on Exercises

You'll experience "in-the-trenches" practice built around actual big data implementations. You'll learn to avoid pitfalls and do it right the first time. Your instructor will help you map the tools and techniques you learn in this class to your own business, so they can be applied in your own organization immediately after the class.

How to use MapReduce in Hadoop?

  • How does it work from languages like Java?
  • How does it work with languages like Ruby?

How to use Yarn within Hadoop?

  • How does it work from languages like Java?
  • How does it work with languages like Ruby?

Overview of HDFS commands

  • Standard file system commands
  • Moving data to and from HDFS

Hands-on activities with Pig

  • Joining Data
  • Filtering Data
  • Storing and Loading Data

Hands-on activities with Hive/HCatalog

  • Storing and Loading Data
  • Select expressions
  • Hive vs SQL

Hands-on activities with Sqoop

  • Running evaluation commands with Sqoop
  • Importing data from relational databases
  • Exporting data to relational databases

Demonstration of Oozie

  • Creating a workflow
  • Running a workflow automatically at regular intervals
  • Running a workflow automatically when some events are triggered

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