Web Development
Python
SQL
JavaScript
Data Science
Artificial Intelligence
IT & Software
IT Certifications
Hacking
Arduino
AWS
3D Modeling
Graphic Design
Photoshop
Drawing
Digital Painting
UX Design
Business
Data Analysis
Communication Skills
Project Management
Leadership & Management
E-Commerce
Digital Marketing
Social Media Marketing
Marketing
SEO
Google Analytics
Branding
Office Productivity
Excel
Finance & Accounting
Bitcoin
Video Editing & Animation
Photography
Adobe Premier Pro
Adobe Lightroom
Personal Development
Health & Fitness
Music
Guitar
Teaching & Academics
Language Learning
Engineering
Lifestyle
168 Best + Free Hadoop Courses & Certificates [2026]
Ranked by Michael Kuhlman, CourseDuck's founder, from student reviews across every provider. How we rank
Our top picks
- The Ultimate Hands-On Hadoop: Tame your Big Data! [Udemy]
- Apache Spark with Scala - Hands On with Big Data! [Udemy]
- Taming Big Data with Apache Spark 4 and Python - Hands On! [Udemy]
- PySpark - Apache Spark Programming for Beginners (2026) [Udemy]
- Hadoop Starter Kit [Udemy]
- Learn Big Data: The Hadoop Ecosystem Masterclass [Udemy]
- GCP: Complete Google Data Engineer and Cloud Architect Guide [Udemy]
- Hive to ADVANCE Hive (Real time usage) :Hadoop querying tool [Udemy]
- Hadoop Developer In Real World [Udemy]
- PySpark & AWS: Master Big Data With PySpark and AWS [Udemy]
About the best Hadoop courses
CourseDuck compares the 168 Hadoop courses listed here from 5 providers, 16 of them free, and ranks them from the 324,000 ratings and reviews their students have left, with the price, length and level of each one side by side. No course pays to be listed or ranked. As featured on Harvard EDU, Stackify and Inc. How we rank
Frequently asked questions
- Udemy and Eduonix are best for practical, low cost and high quality Hadoop courses.
- Coursera, Udacity and EdX are the best providers for a Hadoop certificate, as many come from top Ivy League Universities.
- YouTube is best for free Hadoop crash courses.
- PluralSight, SkillShare and LinkedIn are the best monthly subscription platforms if you want to take multiple Hadoop courses.
- Independent Providers for Hadoop courses & certificates are generally hit or miss.
One of the most important and motivating reasons to learn Big Data and Hadoop is the fact that it brings an array of opportunities to bolster your career to an unprecedented level. As more and more companies turn to Big Data, they are increasingly looking for specialists who can interpret and use data.
- Average
- $129,974 a year
- Middle half earn
- $107,500 to $148,500 25th to 75th percentile
- Full range
- $61,000 to $193,000
Share of US job postings in each pay band; the dark bar holds the average, the striped bars the 25th and 75th percentiles. Source: ZipRecruiter salary data, gathered when this guide was first published in October 2026.
Yes and No. Certified Hadoop developers on average make more money. Having a Hadoop certificate greatly increases the chance of landing an interview and can open otherwise closed doors. Coursera, Udacity and EdX offer excellent certificate options for impressing your future employers. Eduonix, Udemy and several other providers offer certificates, but they aren't as reputable. If you have a Computer Science Degree, certificates are not as important. Still, many employers won't care about certificates, but rather your interview skills, experience and/or skills assessment.
168 Filtered Courses
1
The Ultimate Hands-On Hadoop: Tame your Big Data! (2026)
4.5
A 14.5 hour tour of 25-plus Hadoop ecosystem tools with a sandbox VM for hands-on practice, taught by a former Amazon engineer and rated 4.5 by 31,000 reviewers.
Best for: Engineers, analysts and managers who want a map of the whole Hadoop ecosystem before specializing in one tool.
Pros
Cons
-
- 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
-
- 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
2
Apache Spark with Scala - Hands On with Big Data! (2026)
4.3
Frank Kane's nine-hour Spark 3 course in Scala, built around 20-plus runnable examples that go from movie ratings on your laptop to a real cluster on Amazon EMR.
Best for: Working programmers who need Spark in Scala specifically and learn best by running code, not reading theory.
Pros
Cons
-
- Over 20 runnable examples, from movie ratings to superhero degrees of separation, that reviewers say all work
- Covers the full stack: RDDs, DataFrames and DataSets, EMR clusters, Spark ML, streaming, and GraphX in nine hours
- Refreshed in July 2026 for Spark 3, IntelliJ, and Structured Streaming
- Kane's pragmatic ex-Amazon teaching style gets consistent praise for clarity and realism
-
- Some code is demonstrated rather than explained; beginners say functions like reduce are never properly unpacked
- A recent review says the installation videos and downloadable project files are out of sync
- Advanced examples feel rushed, and the only cloud covered is AWS
3
Taming Big Data with Apache Spark 4 and Python - Hands On! (2026)
4.5
A hands-on PySpark introduction from an ex-Amazon engineer, refreshed for Spark 4 in 2026, that goes from RDDs and DataFrames to a real job on an Amazon EMR cluster.
Best for: Programmers who already write Python or similar and want to learn Spark by running 40-plus worked examples, ending on a cloud cluster.
Pros
Cons
-
- Over 40 worked examples of increasing size, from movie ratings to a superhero social graph
- Updated in August 2026 for Spark 4, including Spark Connect and Pandas-on-Spark
- Takes the same scripts off the desktop and onto an Amazon EMR cluster with Hadoop YARN
- Reviewers describe the explanations as clear and encouraging; one picks Frank Kane's courses over any others on Udemy
-
- One reviewer wants fewer RDD examples and more DataFrame work, which he says matters more today
- Code is mostly provided and walked through, with no quizzes, exercises, or a capstone project
- Setup is shown on Windows; at least one Mac user found the instructions unclear
4
PySpark - Apache Spark Programming for Beginners (2026)
4.7
A patient, example-driven PySpark course rebuilt for 2026 on Databricks Free Edition, covering the DataFrame API end to end and finishing with a capstone project.
Best for: Python developers with some SQL who want to learn Spark's DataFrame API properly before taking on data engineering work.
Pros
Cons
-
- Rebuilt for 2026 and runs on Databricks Free Edition, so there is no local Spark install to fight with
- Reviewers single out the calm pace and detailed explanations; concepts come before the code
- Deep coverage of DataFrame transformations, data types, joins, aggregations, UDFs and unit testing
- Ends with a capstone project of about three hours plus a section on running Spark from a laptop IDE
-
- One reviewer finds the repetition excessive; the same point is often explained more than once
- Supplied notebooks arrive already completed, which removes the practice from the early sections
- No quizzes or coding exercises, and two sections of older videos remain in the syllabus
5
Hadoop Starter Kit (2017)
4.5
A free three-hour Hadoop primer from working consultants, with access to a real multi-node cluster and about 184,000 enrolled students behind its 4.5 rating.
Best for: Newcomers who need the HDFS and MapReduce basics explained before a longer or paid Hadoop course.
Pros
Cons
-
- Free access to the team's multi-node Hadoop cluster so students can run the examples
- Clear, well organized explanations of Hadoop and MapReduce concepts, per reviewers
- Short at 3.3 hours with four quizzes, so it fits into a weekend
- 4.5 rating from over 16,000 reviews, and it costs nothing
-
- Last updated February 2017, so expect older tooling and no Spark or HBase
- Auto-generated captions are hard to follow against the accent, two reviewers say
- One reviewer got a surprise AWS bill following the cluster setup in lecture 14
6
Learn Big Data: The Hadoop Ecosystem Masterclass (2025)
4.5
Master the Hadoop ecosystem using HDFS, MapReduce, Yarn, Pig, Hive, Kafka, HBase, Spark, Knox, Ranger, Ambari, Zookeeper
What You'll Learn
- Process Big Data using batch
- Process Big Data using realtime data
- Be familiar with the technologies in the Hadoop Stack
- Be able to install and configure the Hortonworks Data Platform (HDP)
7
GCP: Complete Google Data Engineer and Cloud Architect Guide (2018)
4.0
The Google Cloud for ML with TensorFlow, Big Data with Managed Hadoop
What You'll Learn
- Deploy Managed Hadoop apps on the Google Cloud
- Build deep learning models on the cloud using TensorFlow
- Make informed decisions about Containers, VMs and AppEngine
- Use big data technologies such as BigTable, Dataflow, Apache Beam and Pub/Sub
8
Hive to ADVANCE Hive (Real time usage) :Hadoop querying tool (2025)
4.5
byJ Garg
In and Out of Apache Hive - From Basic to Advance Hive (Real-world concepts) + Use cases asked in Hive interviews
What You'll Learn
- Learn the complete in-and-out details of Apache Hive from Basic to Advance level.
- Start by exploring the fundamentals of Hive including Hive's Introduction, Architecture, Installation, SQL vs Hive etc.
- Learn Basic Hive concepts to Create Databases & Tables, Insert data, Joins, Views, Mathematical functions, String functions, Conditional statements etc.
- Strength of this course is ADVANCE HIVE, covering those Hive features that are actively used in Real-time projects.
- Partitioning, Bucketing, Explode & Lateral views, Tablesampling, Variables, Optimized Map Joins, User defined functions (UDFs)
- ACID features of Hive, Different types of files in Hive, Custom input formatter, Archiving and Compression techniques etc.
- Learn about various Hive Tableproperties - Skip header & footer, Immutable table, Purge, Null format, Parallelism and ORC table properties.
- Implement Slowly changing Dimensions (SCD 1) using Hive q
9
Hadoop Developer In Real World (2020)
4.6
Free Cluster Access * HDFS * MapReduce * YARN * Pig * Hive * Flume * Sqoop * AWS * EMR * Optimization * Troubleshooting
What You'll Learn
- Understand what is Big Data, the challenges with Big Data and how Hadoop propose a solution for the Big Data problem
- Work and navigate Hadoop cluster with ease
- Install and configure a Hadoop cluster on cloud services like Amazon Web Services (AWS)
- Understand the difference phases of MapReduce in detail
- Write optimized Pig Latin instruction to perform complex data analysis
- Write optimized Hive queries to perform data analysis on simple and nested datasets
- Work with file formats like SequenceFile, AVRO etc
- Understand Hadoop architecture, Single Point Of Failures (SPOF), Secondary/Checkpoint/Backup nodes, HA configuration and YARN
- Tune and optimize slowing running MapReduce jobs, Pig instructions and Hive queries
- Understand how Joins work behind the scenes and will be able to write optimized join statements
- Wherever possible, students will be introduced to difficult questions that are asked in real Ha
10
PySpark & AWS: Master Big Data With PySpark and AWS (2025)
4.4
Mastering AWS & PySpark: Spark, PySpark, AWS, Spark Ecosystem, Hadoop, and Spark Applications [AWS, Hadoop, Pyspark]
What You'll Learn
- The introduction and importance of Big Data.
- Practical explanation and live coding with PySpark.
- Spark applications
- Spark EcoSystem
- Spark Architecture
- Hadoop EcoSystem
- Hadoop Architecture
- PySpark RDDs
- PySpark RDD transformations
- PySpark RDD actions
- PySpark DataFrames
- PySpark DataFrames transformations
- PySpark DataFrames actions
- Collaborative filtering in PySpark
- Spark Streaming
- ETL Pipeline
- CDC and Replication on Going
11
Taming Big Data with MapReduce and Hadoop - Hands On! (2025)
4.4
Learn MapReduce fast by building over 10 real examples, using Python, MRJob, and Amazon's Elastic MapReduce Service.
What You'll Learn
- Understand how MapReduce can be used to analyze big data sets
- Write your own MapReduce jobs using Python and MRJob
- Run MapReduce jobs on Hadoop clusters using Amazon Elastic MapReduce
- Chain MapReduce jobs together to analyze more complex problems
- Analyze social network data using MapReduce
- Analyze movie ratings data using MapReduce and produce movie recommendations with it.
- Understand other Hadoop-based technologies, including Hive, Pig, and Spark
- Understand what Hadoop is for, and how it works