Big Data Essentials: HDFS, MapReduce and Spark RDD from Coursera

Have you ever heard about such technologies as HDFS, MapReduce, Spark? Always wanted to learn these new tools but missed concise starting material? Donâ€™t miss this course either! In this 6-week course you will: - learn some basic technologies of the modern Big Data landscape, namely: HDFS, MapReduce and Spark; - be guided both through systems internals and their applications; - learn about distributed file systems, why they exist and what function they serve; - grasp the MapReduce framework, a workhorse for many modern Big Data applications; - apply the framework to process texts and solve sample business cases; - learn about Spark, the next-generation computational framework; - build a strong understanding of Spark basic concepts; - develop skills to apply these tools to creating solutions in finance, social networks, telecommunications and many other fields. Your learning experience will be as close to real life as possible with the chance to evaluate your practical assignments on a real cluster. No mocking, a friendly considerate atmosphere to make the process of your learning smooth and enjoyable. Get ready to work with real datasets alongside with real masters! Special thanks to: - Prof. Mikhail Roytberg, APT dept., MIPT, who was the initial reviewer of the project, the supervisor and mentor of half of the BigData team. He was the one, who helped to get this show on the road. - Oleg Sukhoroslov (PhD, Senior Researcher at IITP RAS), who has been teaching MapReduce, Hadoop and friends since 2008. Now he is leading the infrastructure team. - Oleg Ivchenko (PhD student APT dept., MIPT), Pavel Akhtyamov (MSc. student at APT dept., MIPT) and Vladimir Kuznetsov (Assistant at P.G. Demidov Yaroslavl State University), superbrains who have developed and now maintain the infrastructure used for practical assignments in this course. - Asya Roitberg, Eugene Baulin, Marina Sudarikova. These people never sleep to babysit this course day and night, to make your learning experience productive, smooth and exciting.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Introduces HDFS, MapReduce, and Spark, which are industry standard in Big Data

Applies MapReduce framework to process texts and solve sample business cases

Provides a strong foundation for understanding Spark basic concepts

Taught by instructors who are recognized for their work in the Big Data field

Uses real datasets and a real cluster for practical assignments

May require students to come in with some basic understanding of programming and data analysis

Reviews summary

Big data fundamentals with hdfs, mapreduce and spark rdd

This course offers a comprehensive introduction to Big Data technologies, including HDFS, MapReduce, and Spark RDD. Students learn the fundamentals of big data architecture and apply them to real-world problems. While some reviewers mention challenges with understanding the instructors' accents and the autograder system, the course is generally well-received for its informative content and practical assignments.

Hands-on assignments

"You can spend many hours just "fixing" your code despite having the good result"

"While the theory is great and the course is fully loaded with the information, the Spark grading is very buggy and unpredictable."

In-depth coverage of concepts

"Course Content is good, but some times the grading tool is getting unresponsive."

Course content needs updating

"Really great course, but needs an update!"

Challenges with autograder

"The Grader was very bad and not easy to debug"

"Bugged grader and complete lack of support from admins of the course"

"Assignments level was good but jupyter sandbox gives problem while downloading notebook and also the result checker gives some issu, produces different output many times , different from what we get in notebook"

Difficulty understanding instructors

"Very difficult to understand the instructors accents"

"The pronunciation of some of the teachers was so bad I had to switch to transcripts."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Big Data Essentials: HDFS, MapReduce and Spark RDD with these activities:

Reach out to experienced Spark users

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Connect with experienced Spark users who can provide guidance and support.

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Identify experienced Spark users in your network or online
Reach out and schedule a time to connect

Complete the Spark Quick Start tutorial

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Get started with Spark quickly by following a hands-on tutorial.

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Follow the steps in the Spark Quick Start tutorial

Practice HDFS commands

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Practice using HDFS commands to reinforce your understanding of how distributed file systems work.

Browse courses on HDFS

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Work through examples in the Apache HDFS tutorial
Create a sample dataset and practice HDFS commands on it

Four other activities

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Participate in a Spark discussion forum

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Connect with other Spark users and learn from their experiences.

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Find a Spark discussion forum
Join the forum and participate in discussions

Solve Spark coding problems

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Test your Spark coding skills by solving problems.

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Register for a coding challenge platform
Filter and solve Spark-related coding problems

Write a blog post about MapReduce

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Demonstrate your understanding of MapReduce by writing a blog post that explains its concepts and applications.

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Research MapReduce and its key concepts
Identify an application of MapReduce in your field of interest
Write a detailed blog post that explains the application using MapReduce

Develop a small-scale Spark application

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Apply your Spark knowledge to a practical project.

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Identify a small-scale data processing task
Design and implement a Spark application to solve the task

Career center

Learners who complete Big Data Essentials: HDFS, MapReduce and Spark RDD will develop knowledge and skills that may be useful to these careers:

Big Data Engineer

Big Data Engineers will directly use the tools and systems taught in this course on a daily basis. For example, they may use Spark to analyze large datasets. By taking this course, one may gain the fundamental knowledge to pursue this role.

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Data Scientist

Data Scientists directly use the tools and systems taught in this course on a regular basis. For example, they may use Spark to analyze large datasets. Gaining exposure to these fundamental tools before working as a Data Scientist may help someone succeed in this role.

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Data Analyst

Data Analysts often use the tools taught in this course on a regular basis. Some Data Analysts use Spark to analyze large datasets. This course introduces the basics of Spark, and may help Data Analysts succeed.

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Data Architect

Data Architects are likely to use the tools taught in this course on a regular basis. This course introduces basic technologies of the modern Big Data landscape, including HDFS, MapReduce, and Spark, all of which are commonly used by Data Architects.

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Software Architect

Software Architects may find that this course is helpful for understanding the most widely-used frameworks and systems, like Hadoop, MapReduce, and Spark. These are crucial systems for working as a Software Architect.

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Software Engineer

Software Engineers who work with Big Data will likely use the skills learned in this course. This course gives an introduction to handling the large datasets that are essential for Big Data. It will also teach how to use systems like HDFS and Hadoop to process and extract value from these large datasets.

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Cloud Architect

Cloud Architects may find that this course is useful for understanding the most widely-used frameworks and systems, like Hadoop, MapReduce, and Spark. These are essential foundations for working as a Cloud Architect.

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Data Engineer

Data Engineers will likely rely on some of the basics taught in this course, like HDFS, which is a distributed file system founded on Hadoop. Both HDFS and Hadoop are some of the most fundamental technologies used in Big Data.

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Business Analyst

Business Analysts play a key role in many companies and some may find that it is useful to have an understanding of Big Data technologies. By taking this course, Business Analysts can learn about some of the most widely-used frameworks and systems, like Hadoop, MapReduce, and Spark.

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Systems Administrator

Some Systems Administrators are responsible for handling Big Data and may find that this course is helpful for understanding the basics of HDFS, MapReduce, and Spark. Being familiar with these systems can help someone succeed in the role of a Systems Administrator.

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Product Manager

Product Managers may find that this course introduces them to some of the most widely-used frameworks and systems, like Hadoop, MapReduce, and Spark, which are often used for product development and design.

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Machine Learning Engineer

Machine Learning Engineers may find that this course is helpful for learning how to use Spark to analyze large datasets. Spark is one of the most common tools used to analyze big data, and therefore may be useful to Machine Learning Engineers.

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Quantitative Analyst

Quantitative Analysts may find that this course is helpful for understanding the basics of HDFS, which is a distributed file system often employed by Quantitative Analysts.

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Database Administrator

Database Administrators may find that this course is helpful for understanding the basics of HDFS, which is a distributed file system often employed by Database Administrators.

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Statistician

Statisticians may find that this course is useful for understanding the basics of HDFS, which is a distributed file system. Some Statisticians may need to use HDFS in their daily work.

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