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The Ultimate Hands-On Hadoop: Tame your Big Data! (Udemy.com)

Data Engineering and Hadoop tutorial with MapReduce, HDFS, Spark, Flink, Hive, HBase, MongoDB, Cassandra, Kafka + more!

Created by: Sundog Education by Frank Kane

Last updated July 2026

Our take

Based on the ratings of 31,163 students, a sample of their written reviews and the syllabus, as the course stood in July 2026. No course pays to be reviewed.

Frank Kane's Hadoop course is a tour of the whole ecosystem, not a deep study of one tool. Across 14.5 hours and 103 lectures it touches more than 25 technologies: HDFS, MapReduce, Pig, Spark, Hive, HBase, Cassandra, MongoDB, Kafka, Flink, Storm and more. Most get a short explanation and a hands-on activity in a Hortonworks sandbox running on a local PC. It suits engineers, analysts and managers who need to understand how these pieces fit together. Kane, a former Amazon and IMDb senior manager, keeps things clear, and reviewers describe his teaching as concise and easy to follow.

Students praise the structure. It carries a 4.5 rating from 31,000 reviews, and the sampled reviews keep coming back to clear explanations and exercises that make each tool feel real. The complaints cluster around the lab setup. The Hortonworks sandbox needs a 64-bit x86 machine with 8GB of free RAM, and it will not run on Apple Silicon Macs, a point two reviewers raise. Others say setup took hours of googling and Q&A digging, and one one-star review argues the course should be retired or rebuilt around a cloud service. One reviewer also notes that some of the tools covered have faded from use.

The listing says it was updated in July 2026, so the lectures are being maintained even if the sandbox is old. It sits alongside several Spark-only courses, which go deeper on one engine; this one is the map of the territory. If you want a working mental model of Hadoop and its neighbors before picking a specialty, it's a sound choice. If your goal is hands-on Spark skills or a modern cloud stack, start with one of the Spark courses instead.

Best forEngineers, analysts and managers who want a map of the whole Hadoop ecosystem before specializing in one tool.
Skip it ifYou have an Apple Silicon Mac or under 8GB of free RAM, or you want deep Spark skills rather than a broad survey.

Pros

  • Covers more than 25 tools in 14.5 hours, most with a short explanation and a hands-on exercise
  • Reviewers call Frank Kane's explanations clear and concise, even at a demanding level
  • Ships with a Hortonworks sandbox VM so every tool can be tried on a local machine
  • Still being updated in 2026 despite a 2017 launch, with 24 caption tracks

Cons

  • Sandbox VM needs 8GB of free RAM on an x86 machine and does not run on Apple Silicon Macs
  • Reviewers report hours lost to setup and hunting for archived VirtualBox images
  • Some tools covered are no longer common in modern stacks, and the labs run on premise, not in the cloud
Very clear explanations! Even though not all the technologies are still relevant in the modern Big Data landscape, it's a good conceptual presentation that helps you see how things connect to each other
Lavinia Maria Bucatariu, a student

Listed as all levels, but the labs assume some Python or Scala and Linux command line; managers can follow the lectures without them.

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What you will learn

  • Design distributed systems that manage "big data" using Hadoop and related data engineering technologies.
  • Use HDFS and MapReduce for storing and analyzing data at scale.
  • Use Pig and Spark to create scripts to process data on a Hadoop cluster in more complex ways.
  • Analyze relational data using Hive and MySQL
  • Analyze non-relational data using HBase, Cassandra, and MongoDB
  • Query data interactively with Drill, Phoenix, and Presto
  • Choose an appropriate data storage technology for your application
  • Understand how Hadoop clusters are managed by YARN, Tez, Mesos, Zookeeper, Zeppelin, Hue, and Oozie.
  • Publish data to your Hadoop cluster using Kafka, Sqoop, and Flume
  • Consume streaming data using Spark Streaming, Flink, and Storm

Course content

12 sections · 103 lectures · 15 hours of video 9 articles

  1. 1Learn all the buzzwords! And install the Hortonworks Data Platform Sandbox. 3 free previews8 lectures · 47 min
  2. 2Using Hadoop's Core: HDFS and MapReduce 2 free previews12 lectures · 1.5 hours
  3. 3Programming Hadoop with Pig 1 free preview7 lectures · 56 min
  4. 4Programming Hadoop with Spark 2 free previews8 lectures · 1.3 hours
  5. 5Using relational data stores with Hadoop 1 free preview10 lectures · 1.1 hours
  6. 6Using non-relational data stores with Hadoop 1 free preview13 lectures · 2.5 hours
  7. 7Querying your Data Interactively 2 free previews9 lectures · 1.4 hours
  8. 8Managing your Cluster 1 free preview13 lectures · 2 hours
  9. 9Feeding Data to your Cluster 1 free preview6 lectures · 55 min
  10. 10Analyzing Streams of Data 1 free preview8 lectures · 1.3 hours

Who it is for

The instructor says it suits

  • Software engineers and programmers who want to understand the larger Hadoop ecosystem, and use it to store, analyze, and vend "big data" at scale.
  • Project, program, or product managers who want to understand the lingo and high-level architecture of Hadoop.
  • Data analysts and database administrators who are curious about Hadoop and how it relates to their work.
  • System architects who need to understand the components available in the Hadoop ecosystem, and how they fit together.

What you need before you start

  • You will need access to a x86-based PC running 64-bit Windows, MacOS, or Linux with an Internet connection and at least 8GB of *free* (not total) RAM, if you want to participate in the hands-on activities and exercises. If your PC does not meet these requirements or you only have an M1-based Mac available, you can still follow along in the course without doing hands-on activities.
  • Some activities will require some prior programming experience, preferably in Python or Scala.
  • A basic familiarity with the Linux command line will be very helpful.

How it compares

Against the other 150 rated Udemy courses in Hadoop on CourseDuck:

  • Rating4.5 out of 5higher than 68% of them
  • Reviews31,163#1 of 151 by number of reviews
  • Length14.5 hourslonger than 82% of them
  • Last updatedJuly 2026more recently than 88% of them

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Course Description

The world of Hadoop and "Big Data" can be intimidating - hundreds of different technologies with cryptic names form the Hadoop ecosystem. With this Hadoop tutorial, you'll not only understand what those systems are and how they fit together - but you'll go hands-on and learn how to use them to solve real business problems!

Learn and master the most popular data engineering technologies in this comprehensive course, taught by a former engineer and senior manager from Amazon and IMDb. We'll go way beyond Hadoop itself, and dive into all sorts of distributed systems you may need to integrate with.


  • Install and work with a real Hadoop installation right on your desktop with Hortonworks (now part of Cloudera) and the Ambari UI

  • Manage big data on a cluster with HDFS and MapReduce

  • Write programs to analyze data on Hadoop with Pig and Spark

  • Store and query your data with Sqoop, Hive, MySQL, HBase, Cassandra, MongoDB, Drill, Phoenix, and Presto

  • Design real-world systems using the Hadoop ecosystem

  • Learn how your cluster is managed with YARN, Mesos, Zookeeper, Oozie, Zeppelin, and Hue

  • Handle streaming data in real time with Kafka, Flume, Spark Streaming, Flink, and Storm

Spark and Hadoop developers are hugely valued at companies with large amounts of data; these are very marketable skills to learn.

Almost every large company you might want to work at uses Hadoop in some way, including Amazon, Ebay, Facebook, Google, LinkedIn, IBM,  Spotify, Twitter, and Yahoo! And it's not just technology companies that need Hadoop; even the New York Times uses Hadoop for processing images.

This course is comprehensive, covering over 25 different technologies in over 14 hours of video lectures. It's filled with hands-on activities and exercises, so you get some real experience in using Hadoop - it's not just theory.

You'll find a range of activities in this course for people at every level. If you're a project manager who just wants to learn the buzzwords, there are web UI's for many of the activities in the course that require no programming knowledge. If you're comfortable with command lines, we'll show you how to work with them too. And if you're a programmer, I'll challenge you with writing real scripts on a Hadoop system using Scala, Pig Latin, and Python.

You'll walk away from this course with a real, deep understanding of Hadoop and its associated distributed systems, and you can apply Hadoop to real-world problems. Plus a valuable completion certificate is waiting for you at the end! 

Please note the focus on this course is on application development, not Hadoop administration. Although you will pick up some administration skills along the way.

Knowing how to wrangle "big data" is an incredibly valuable skill for today's top tech employers. Don't be left behind - enroll now!


  • "The Ultimate Hands-On Hadoop... was a crucial discovery for me. I supplemented your course with a bunch of literature and conferences until I managed to land an interview. I can proudly say that I landed a job as a Big Data Engineer around a year after I started your course. Thanks so much for all the great content you have generated and the crystal clear explanations. " - Aldo Serrano

  • "I honestly wouldn’t be where I am now without this course. Frank makes the complex simple by helping you through the process every step of the way. Highly recommended and worth your time especially the Spark environment.   This course helped me achieve a far greater understanding of the environment and its capabilities.  Frank makes the complex simple by helping you through the process every step of the way. Highly recommended and worth your time especially the Spark environment." - Tyler Buck

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Instructor Details

Sundog Education by Frank Kane

Sundog Education's mission is to make highly valuable career skills in data engineering, data science, generative AI, AWS, and machine learning accessible to everyone in the world. Our consortium of expert instructors shares our knowledge in these emerging fields with you, at prices anyone can afford. 

As a senior manager at Amazon during its early years, Frank Kane led large technical teams, managed multi-year projects, and served as a "bar-raiser" - interviewing thousands and hiring hundreds across the company.

Frank holds 26 issued patents in machine learning and personalization. After 9 years at Amazon and IMDb, he founded Sundog Education and has since taught over 1.1 million people in AI, ML, AWS, system design, and tech leadership. Frank has been teaching full-time for over 10 years - but is always building projects on the side with the latest emerging technologies, to better explain them to you!

Our teaching style is to simplify complex concepts - once you cut through all the notation and jargon, most of AI and machine learning is pretty easy to understand. We make these advanced topics accessible to anyone with a modest technical background, always using plain English and examples to drive it home. We make learning easy, hands-on, and fun.

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Reviews

4.5

31,163 ratings on Udemy

Select a bar to show only those reviews.Select the bar again to show every rating.

By Lavinia Maria Bucatariu on 7/21/2026

Very clear explanations! Even though not all the technologies are still relevant in the modern Big Data landscape, it's a good conceptual presentation that helps you see how things connect to each other and also connect the dots later on when transitioning to whatever technology is more relevant to a specific project.

By Anonymized User on 3/23/2021

I learned about a different technologies, however the practical aspect of this course was not easy, and to be frank, quite frustrating at times. 1. It takes a really long time to set everything up, and get it to work. There should be a note somewhere on the course page how many extra hours this course would take, with the hands-on component. 2. A lot of issues with newer versions, and even the older version of virtual box, as it is now outdated. I often found myself google searching, and searching within the Q&A to find the answers to my issues. I definitely spent hours trying to run some of the components (MongoDB, Cassandra, Spark MLLib). I truly appreciated all the feedback given by the other students. It's a well-rounded courses, it just needs some updates and preferably a heads-up, based on more recent experiences. Thank you for putting it together!

By Aurélio Silva on 6/10/2019

Great course. The lectures are very well put together both in sequence and format. I finished the course with the feeling of having a good eneral idea of how these technologies relate with each other. I just don't give it 5 stars because in my opinion the instructors should contribute more to solve common problems of the activities in the discussion sections. For more than once I encountered some issue that somebody had posted before, and no valuable solution was provided in some cases

By Srini Bsaspec on 5/13/2019

Overall good course, but only the issue I faced was that I couldn't download and run the Hortonworks Virtual Machine because it was of huge size. Can you please come up with the solution if there is any VM which works for Big Data platforms and which also is smaller in size?

By Tyler Buck on 12/4/2018

This course is a great to full understand The Hadoop big data environment! When I started at Foot Locker, I didn’t understand big data at all let alone the technologies. This course helped me achieve a far greater understanding of the environment and its capabilities. I honestly wouldn’t be where I am now with out this course. Frank makes the complex simple by helping you through the process every step of the way. Highly recommended and worth your time especially the Spark environment. I am not reviewing to boost purchases. I’m reviewing because the course is that good.

By Amit Gupta on 9/14/2018

Covers most of the topics for Big Data. But very basic material on every topic. Most of the time the code doesn't work and the environment suggested for the course will crash. What will help -- A proper environment where students can test run the code and little more details for most of the topics. Edit: After completing the course I feel it's a very good course for bigeners and also for experienced people. It not only covers a Ranga of technologies but shows how to use them together. Plus it concludes with good examples of integration in practical scenarios.

By Vipul Bhatnagar on 10/6/2017

The course is comprehensive and gives a very splendid introduction of all that is out there. It is recommended for those who have absolutely no idea at all of the hadoop ecosystem. It only gives a small flavor of all the different components and it is up to the listener to branch out and do the in-depth study.

By Johann Liang on 8/22/2017

Frank did a great job in teaching a long list of Hadoop technologies in a practical and easy to understand manner. I really appreciate the upbeat tone throughout the lectures and the hands-on examples that are there for almost every technology introduced. Thanks, Frank! Keep up the great work!

By Duy Khang Ly on 6/24/2017

Definitely one of the greatest introduction courses about the topic. Frank breaks the whole picture down into bits, and walk us through how to digest them. I feel I know enough concepts to be dangerous, and will enjoy exploring further in this journey of Big Data. Thanks a lot!

By Vishal Kankatala on 3/19/2017

I was extremely pleased with the content and breadth of this tutorial. However, I'd recommend adding more exercises and lesser hints throughout the program. I acknowledge this is an introductory course but the design problems at the end were real good and I felt the course didn't prep me enough to take on that challenge.

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Overall Score : 90 / 100