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.

Browse courses on Spark

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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.

Browse courses on Spark

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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.

Browse courses on Spark

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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.

Browse courses on MapReduce

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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.

Browse courses on Spark

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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.

See salaries and explore the career path for Big Data Engineer

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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Reading list

We've selected eight books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Big Data Essentials: HDFS, MapReduce and Spark RDD.

Fundamentals of Machine Learning for Predictive...

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Provides a comprehensive overview of data science and big data analytics, including how to use Hadoop and Spark for data processing. It valuable resource for anyone who wants to learn more about data science and big data analytics and how to use Hadoop and Spark effectively.

Fundamentals of Machine Learning for Predictive...

Paperback

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Fundamentals of Machine Learning for Predictive...

Kindle Edition

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Learning Spark

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Provides a practical introduction to big data with Spark and Scala, including how to use Spark for data processing. It valuable resource for anyone who wants to learn more about big data with Spark and Scala and how to use it effectively.

Learning Spark: Lightning-Fast Big Data Analysis

Paperback

Big Data and Hadoop

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Provides a comprehensive overview of big data analytics, including how to use Hadoop and Spark for data processing. It valuable resource for anyone who wants to learn more about big data analytics and how to use Hadoop and Spark effectively.

Big Data and Hadoop

Kindle Edition

Big Data Analytics with Java

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Provides a comprehensive overview of big data analytics with Java, including how to use Hadoop and Spark for data processing. It valuable resource for anyone who wants to learn more about big data analytics with Java and how to use Hadoop and Spark effectively.

Big Data Analytics with Java: Data analysis,...

Paperback

$$$

Big Data Analytics with Java: Data analysis,...

Kindle Edition

Spark: The Definitive Guide

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Provides a comprehensive overview of Spark, including its architecture, components, and how to use it for data processing. It valuable resource for anyone who wants to learn more about Spark and how to use it effectively.

Spark: The Definitive Guide: Big Data Processing...

Paperback

Spark: The Definitive Guide: Big Data Processing...

Kindle Edition

Learning Spark

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Provides a practical introduction to Spark, including how to use it for data processing, machine learning, and graph analysis. It valuable resource for anyone who wants to learn more about Spark and how to use it effectively.

Learning Spark: Lightning-Fast Big Data Analysis

Paperback

Hadoop: The Definitive Guide

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Provides a comprehensive overview of Hadoop, including its architecture, components, and how to use it for data processing. It valuable resource for anyone who wants to learn more about Hadoop and how to use it effectively.

Hadoop: The Definitive Guide: Storage and Analysis...

Paperback

Hadoop: The Definitive Guide

Kindle Edition

Hadoop: The Definitive Guide

Paperback

Hadoop: The Definitive Guide

Paperback

Data-Intensive Text Processing with MapReduce

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Provides a detailed introduction to MapReduce, including its programming model, how to use it to process data, and how to optimize MapReduce programs. It valuable resource for anyone who wants to learn more about MapReduce and how to use it effectively.

Data-Intensive Text Processing with MapReduce...

Paperback

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