Exploring Spark's GraphX
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Course
Exploring Spark's GraphX
Build valuable skills for graph analytics with Spark GraphX. Discover the useful collection of graph algorithms and builds it offers for simplifying tasks. Learn how to visualize data using various graph operators.
Build in-demand skills in graph analytics and boost your data science career.
Self-Paced
Mentored
BEGINNER
Duration
3-4 hours/ week
Fee
Spark GraphX offers a growing collection of graph algorithms and builders that simplify graph analytics tasks. As a distributed graph computing engine it extends Spark RDD. Examples of the graph algorithms it provides are PageRank, Connected Components, and triangle counting. Data engineers looking to perform both graph analytics and ETL (extract, transform, and load), as well as do graph analysis on data that is not in graph form, will find Spark GraphX an extremely useful tool.
During this course, you will explore GraphX components and the background of graph-parallel operations. You will discover how Spark implements this with RDDs and how it compares vs data parallel operations. You will also learn how to visualize data using various graph operators.
For data scientists and data engineers keen to build their competencies, this Spark GraphX course will give you the skills you need to simplify and speed up graph analytics tasks.
This 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. It is anticipated that it takes 3-4 hours to complete. 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.
Course Outline
Why Learn with SkillUp Online?
We believe every learner is an individual and every course is an opportunity to build job-ready skills. Through our human-centered approach to learning, we will empower you to fulfil your professional and personal goals and enjoy career success.
Reskilling into tech? We’ll support you.
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Cross-skilling for your career? We’ll guide you.
Personalized Mentoring & Support
1-on-1 mentoring, live classes, webinars, weekly feedback, peer discussion, and much more.
Practical Experience
Hands-on labs and projects tackling real-world challenges. Great for your resumé and LinkedIn profile.
Best-in-Class Course Content
Designed by the industry for the industry so you can build job-ready skills.
Job-Ready Skills Focus
Competency building and global certifications employers are actively looking for.
Course Offering
Type of certificate
IBM Certificate
About this course
04 Modules
03 Skills
Includes
Discussion space
04 Labs
04 Quiz
01 Final exam
05 Videos
Create
Text analytics and streams
Data types
Clusters
Exercises to explore
Modifying GraphX
Neighborhood aggregation and caching in GraphX
Visualizing GraphX and exploring graph operators
Graph-parallel
Overview of high value big data
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FAQs
GraphX is a new component in Spark that is designed to work with graphs and graph-parallel calculations. Overall, GraphX improves on the Spark RDD by offering an additional graph abstraction, which is a directed multigraph with characteristics connected to each node and edge.
GraphX provides built-in operators and algorithms. This makes it easy to execute analytics on graph data. Additionally, the caching and un-caching of graph data is also supported, which facilitates a reduction in calculation times when a graph is called numerous times in a single session.
After completing this course, you will have a better understanding of big data and its four V's; volume, velocity, variety, and veracity. You will be able to develop text analytics and streams, datatypes, and clusters, and you will also have a good grasp of big data governance.
This course comprises four purposely designed modules that take you on a carefully defined learning journey.
Yes, Exploring Spark's GraphX course by SkillUp Online is fully mentored. Throughout the course, you will have access to essential guidance and support. We provide a dedicated discussion space where you may ask questions, talk with your peers, and work out problems. You may also have access to live seminars and webinars, depending on your subscription plan, which are a great way to address concerns with your mentor and ask questions.
There are no prerequisites for taking this course.
Yes, you will be issued an IBM Certificate when you successfully finish the course. However, to get the certificate, you must take all the quizzes along with final exam and score an average of 70%.
Yes, Exploring Spark's GraphX is a 100% online course. All you need is a good connection to the internet to access the course materials.
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.
Course Offering
Type of certificate
IBM Certificate
About this course
04 Modules
03 Skills
Includes
Discussion space
04 Labs
04 Quiz
01 Final exam
05 Videos
Create
Text analytics and streams
Data types
Clusters
Exercises to explore
Modifying GraphX
Neighborhood aggregation and caching in GraphX
Visualizing GraphX and exploring graph operators
Graph-parallel
Overview of high value big data
This course has been created by
Newsletters & Updates
Subscribe to get the latest tech career trends, guidance, and tips in your inbox.