Course HighlightsCOURSE
Hadoop 101

Hadoop 101

Build your knowledge of Hadoop's architecture and core components. Explore using MapReduce and the Hadoop Distributed File System (HDFS). Learn how to modify configuration parameters.

Take an important step towards building critical skills for the fast-growing fields of big data and data science.

Hadoop 101 Highlights

  Course duration

Duration

  • 2 weeks, online
    3-4 hours/week
  Course Fee

Fee

US$ 99 - US$ 199

Course duration

Duration

  • 2 weeks, online
    3-4 hours/week
Course Fee

Fee

US$ 99 - US$ 199

Apache Hadoop is a popular framework for enabling applications to collect data from various locations and in various formats. Businesses use it to facilitate the execution of distributed processes against large amounts of data. It achieves this by allowing the clustering of multiple computers to analyze massive datasets quickly.

During this course, you will be introduced to the basics of Apache Hadoop. You will explore its architecture and core components, including MapReduce and the Hadoop Distributed File System (HDFS). You will learn how to add and remove nodes from Hadoop clusters, how to check available disk space on each node, and how to modify configuration parameters. Plus, you will learn about other Apache projects that are part of the Hadoop ecosystem, including Pig, Hive, HBase, ZooKeeper, Oozie, Sqoop, and Flume.

For individuals keen to build core skills for the big data domain, therefore, Hadoop 101 is an excellent place to start for this leg of your data science journey.

This IBM certified course comprises four purposely designed modules that take you on a carefully defined learning journey.

It is a self-paced course, which means it is not run to a fixed schedule with regard to completing modules or submitting assignments. To give you an idea of how long the course takes to complete, it is anticipated that if you work 3-4 hours per week, you will complete the course in 2 weeks. However, as long as the course is completed by the end of your enrollment, you can work at your own pace. And don’t worry, you’re not alone! You will be encouraged to stay connected with your learning community and mentors through the course discussion space.

The materials for each module are accessible from the start of the course and will remain available for the duration of your enrollment. Methods of learning and assessment will include videos, reading material, and online exam questions.

As part of our mentoring service you will have access to valuable guidance and support throughout the course. We provide a dedicated discussion space where you can ask questions, chat with your peers, and resolve issues. Depending on the payment plan you have chosen, you may also have access to live classes and webinars, which are an excellent opportunity to discuss problems with your mentor and ask questions. Mentoring services will vary across packages.

Once you have successfully completed the course, you will earn your IBM Certificate.

After completing this course students will be able to:

  • Understand Hadoop architecture including MapReduce and HDFS.
  • Use the Hadoop file system shell and the Ambari Console to work with HDFS.
  • Start and stop Hadoop components.
  • Add/remove a node to/from a Hadoop cluster.
  • Modify Hadoop configuration parameters.
  • Individuals keen to learn Hadoop concepts and components.
  • College graduates who want to start their career in big data and Hadoop.
  • Experienced developers in big data seeking to upskill in Hadoop.
  • Knowledge of big data concepts.
  • Have taken the Introduction to Big Data course.
  • Know some basic Linux administration and commands.

Course Outline

General Information
Learning Objectives
Syllabus
Grading Scheme
Change Log
Copyrights and Trademarks
Learning Objectives
What is Hadoop? - Part 2 (4:17)
Lab 1a : Installation of Java
Graded Review Questions (3 Questions) Review Questions
Learning Objectives
Hadoop Architecture - Part 1 (7:03)
Hadoop Architecture - Part 2 (4:49)
HDFS Command Line (3:15)
Hadoop Architecture - Lab Solutions (Cloud)
Graded Review Questions (3 Questions) Review Questions
Learning Objectives
Hadoop Administration [Optional]
Hadoop Administration [Optional]
Graded Review Questions (3 Questions) Review Questions
Learning Objectives
Hadoop Components - Part I - MapReduce (4:31)
Hadoop Components - Part II - Pig and Hive (3:56)
Hadoop Components - Part III - Flume, Sqoop, and Oozie (3:51)
Graded Review Questions (3 Questions) Review Questions
Instructions
Final Exam (18 Questions) Timed Exam Final Exam
Course Certificate

Earn your certificate

Once you have completed this course, you will earn your certificate.

Preview digital certificate
Hadoop 101

FAQs

Hadoop is an open-source software framework for storing data and running applications on clusters of hardware. It offers immense storage for nearly any type of data, considerable processing power, and the ability to handle a high number of concurrent processes or jobs at the same time.

As the adoption of big data continues to grow, companies are increasingly looking for Hadoop professionals who are capable of interpreting and utilizing data. This means that Hadoop is a sector that provides several options to develop and advance your professional career. Put simply, therefore, it is a valuable skill to master.

You will gain a better understanding of Hadoop's architecture and fundamental components. You will investigate the use of MapReduce and the Hadoop Distributed File System (HDFS). Plus, you will learn how to make changes to configuration parameters.

This course is designed for individuals who are interested in learning about Hadoop uses and components. Individuals who already work in tech, and graduates who wish to get their foot in the door in the big data and Hadoop arena, are eligible to enroll in this Hadoop 101 course. The course is also open to experienced engineers in the big data field who want to improve their Hadoop skills.

You must be familiar with the ideas of big data. Additionally, we recommend that you have completed the Introduction to Big Data course. Knowledge of fundamental Linux administration and commands is an added plus.

You will be able to access the course materials through your dashboard as soon as you enroll in this course.

Yes, there are hands-on labs in this course. These hands-on labs assist learners in the development of problem-solving and critical-thinking abilities.

This course is self-paced, which means that you can work at a pace that suits you. It does not follow a predetermined timetable, unlike scheduled live sessions. You are free to work at your own speed if you complete the modules and the course before the deadline.