Big Data Essentials: HDFS MapReduce and Spark RDD faq

instructor Instructor: / instructor-icon
duration Duration: 41.00 duration-icon

This course provides an introduction to the essential big data technologies, HDFS, MapReduce and Spark RDD. Learners will gain the knowledge needed to start working with big data, enabling them to quickly get up to speed.

ADVERTISEMENT

Course Feature Course Overview Course Provider Discussion and Reviews
Go to class

Course Feature

costCost:

Free

providerProvider:

Coursera

certificateCertificate:

Paid Certification

languageLanguage:

English

start dateStart Date:

7th Mar, 2022

Course Overview

❗The content presented here is sourced directly from Coursera platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.

Updated in [March 06th, 2023]

This course, Big Data Essentials: HDFS MapReduce and Spark RDD, is designed to provide learners with a comprehensive introduction to the modern Big Data landscape. Learners will gain an understanding of the fundamentals of distributed file systems, the MapReduce framework, and the Spark computational framework. Through practical assignments, learners will gain hands-on experience in applying these tools to create solutions in finance, social networks, telecommunications, and many other fields.

Learners will gain an understanding of the basics of HDFS, MapReduce, and Spark, including their internals and applications. They will learn how to use the MapReduce framework to process texts and solve sample business cases. They will also learn how to use Spark to build strong understanding of its basic concepts and develop skills to apply these tools to create solutions.

In addition, learners will have the opportunity to evaluate their practical assignments on a real cluster, providing them with a real-life experience. With the help of experienced instructors, learners will be able to gain a comprehensive understanding of Big Data technologies and apply them to their own projects.

[Applications]
The application of this course can be seen in various fields such as finance, social networks, telecommunications and many others. After completing this course, students will have a strong understanding of HDFS, MapReduce and Spark, and the skills to apply these tools to create solutions in the aforementioned fields. They will also be able to evaluate their practical assignments on a real cluster.

[Career Paths]
1. Big Data Engineer: Big Data Engineers are responsible for designing, developing, and maintaining the infrastructure and systems that store and process large amounts of data. They must be knowledgeable in the latest Big Data technologies such as HDFS, MapReduce, and Spark RDD, and have experience in developing and deploying distributed applications. As the demand for Big Data solutions continues to grow, Big Data Engineers will be in high demand.

2. Big Data Analyst: Big Data Analysts are responsible for analyzing large amounts of data to uncover trends and insights. They must be knowledgeable in data mining, machine learning, and statistical analysis techniques, and have experience in working with large datasets. As the need for data-driven decision making increases, Big Data Analysts will be in high demand.

3. Data Scientist: Data Scientists are responsible for developing and deploying data-driven solutions to solve complex business problems. They must be knowledgeable in data mining, machine learning, and statistical analysis techniques, and have experience in working with large datasets. As the need for data-driven solutions increases, Data Scientists will be in high demand.

4. Data Visualization Specialist: Data Visualization Specialists are responsible for creating visual representations of data to help people understand and interpret complex datasets. They must be knowledgeable in data visualization tools and techniques, and have experience in creating interactive visualizations. As the need for data-driven insights increases, Data Visualization Specialists will be in high demand.

[Education Paths]
1. Bachelor of Science in Computer Science: This degree program focuses on the fundamentals of computer science, such as programming, software engineering, and computer architecture. It also covers topics such as artificial intelligence, data structures, and algorithms. This degree is ideal for those interested in developing and managing software applications and systems. Developing trends in this field include the use of machine learning and artificial intelligence to create more efficient and powerful software applications.

2. Master of Science in Data Science: This degree program focuses on the analysis and interpretation of large datasets. It covers topics such as data mining, machine learning, and statistical analysis. This degree is ideal for those interested in working with large datasets to uncover insights and trends. Developing trends in this field include the use of natural language processing and deep learning to uncover more complex patterns in data.

3. Master of Science in Big Data Analytics: This degree program focuses on the analysis and interpretation of large datasets. It covers topics such as data mining, machine learning, and statistical analysis. This degree is ideal for those interested in working with large datasets to uncover insights and trends. Developing trends in this field include the use of predictive analytics and data visualization to uncover more complex patterns in data.

4. Doctor of Philosophy in Big Data: This degree program focuses on the research and development of new technologies and methods for analyzing and interpreting large datasets. It covers topics such as data mining, machine learning, and statistical analysis. This degree is ideal for those interested in advancing the field of big data and developing new methods for uncovering insights and trends. Developing trends in this field include the use of artificial intelligence and blockchain technology to create more efficient and secure data analysis systems.

Course Provider

Provider Coursera's Stats at AZClass

Rating Grade: B This is a trending provider perfect for gaining traction and maybe a good option for users who are looking for a reliable source of learning content.

Discussion and Reviews

0.0   (Based on 0 reviews)

Start your review of Big Data Essentials: HDFS MapReduce and Spark RDD

faq FAQ for Hadoop Courses

Q1: Does the course offer certificates upon completion?

Yes, this course offers a free certificate. AZ Class have already checked the course certification options for you. Access the class for more details.

Q2: How do I contact your customer support team for more information?

If you have questions about the course content or need help, you can contact us through "Contact Us" at the bottom of the page.

Q3: Can I take this course for free?

Yes, this is a free course offered by Coursera, please click the "go to class" button to access more details.

Q4: How many people have enrolled in this course?

So far, a total of 0 people have participated in this course. The duration of this course is 41.00 hour(s). Please arrange it according to your own time.

Q5: How Do I Enroll in This Course?

Click the"Go to class" button, then you will arrive at the course detail page.
Watch the video preview to understand the course content.
(Please note that the following steps should be performed on Coursera's official site.)
Find the course description and syllabus for detailed information.
Explore teacher profiles and student reviews.
Add your desired course to your cart.
If you don't have an account yet, sign up while in the cart, and you can start the course immediately.
Once in the cart, select the course you want and click "Enroll."
Coursera may offer a Personal Plan subscription option as well. If the course is part of a subscription, you'll find the option to enroll in the subscription on the course landing page.
If you're looking for additional Hadoop courses and certifications, our extensive collection at azclass.net will help you.

close

To provide you with the best possible user experience, we use cookies. By clicking 'accept', you consent to the use of cookies in accordance with our Privacy Policy.