18 Best Hadoop Courses & Classes Online To Take In 2023
Dive deep into the world of Hadoop and Big Data analysis with our curated list of the 18 best online courses and classes for 2023, handpicked after evaluating 246 popular courses, nearly 300,000 reviews, and over 3.8 million enrolled students to help you master the Hadoop platform and propel your career to new heights.
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With an overwhelming number of Hadoop courses available online, finding the perfect one to suit your needs might seem quite daunting. To simplify this task, we thoroughly researched 246 popular Hadoop courses from various providers, taking into account a staggering 3,804,505 enrolled students who left 297,840 ratings and reviews. Our team evaluated and handpicked the best courses by rating, reviews, enrollments, learners' opinions, valuable and engaging content, comprehensive curriculum, release date, and affordability, combined with our own expertise and experience.
In this article, you will discover the 18 Best Hadoop Courses & Classes Online curated for a wide range of skillsets and requirements, allowing you to dive deep into the world of Hadoop and Big Data analysis. Be assured, every single course mentioned here has passed our rigorous selection process, designed to help you master the Hadoop platform, its application framework, and associated tools, propelling your career to new heights. Embark on this learning journey, armed with the knowledge of our carefully chosen recommendations.
The Hadoop Starter Kit is designed to make learning Hadoop both easy and enjoyable, guiding students through the core components of this powerful big data framework. By enrolling in this course, students will not only gain valuable knowledge, but they will also have the opportunity to utilize a multi-node Hadoop training cluster, which enables them to apply their newly acquired skills in a practical, real-world distributed environment.
Instruction is provided by a group of passionate Hadoop consultants who have experience in working with big data technologies. The course covers essential topics such as big data fundamentals, storage and computation challenges, HDFS, MapReduce, as well as introductions to Apache Pig and Hive. Students will gain hands-on experience by working with HDFS, writing MapReduce programs in Java, and using Pig and Hive to calculate the maximum closing price for stock symbols from a stock dataset. This comprehensive and engaging approach to learning Hadoop ensures a thorough understanding of the technology and its practical applications in the big data industry.
This course is best for those looking to gain a solid understanding of the Hadoop platform, its application frameworks, and practical experience in using Hive, MapReduce, and more for big data analysis and processing. Additionally, it offers a practical understanding of the Hadoop Ecosystem and distributed systems for Big Data processing.
This course offers a comprehensive introduction to the essentials of Big Data and Hadoop, providing valuable insight into the world of Big Data technologies. Designed for those who may find the topic a bit cryptic, the course aims to build a fundamental understanding of Big Data challenges and Hadoop as a viable solution. By covering aspects such as the history and development of Hadoop, its unique and powerful capabilities, and the difference between data science and data engineering, participants will gain a solid foundation in the subject.
Additionally, the course demystifies the Hadoop landscape by discussing major players and vendors such as Cloudera, MapR, and Hortonworks. This essential knowledge will be beneficial for individuals associated with Big Data and Hadoop in various capacities, including large and small businesses, as well as for personal growth and understanding. By participating in this course, you'll be better equipped to untangle the complexities of Big Data and unlock its potential for problem-solving and innovation.
This course is ideal for building a strong foundation in Big Data and Hadoop, enabling participants to gain a deep understanding of the technologies and solutions in this fast-evolving field. It also demystifies the Hadoop landscape by highlighting key players and vendors.
This comprehensive course dives deep into the world of Hadoop and Big Data, demystifying the various technologies that form the Hadoop ecosystem. Throughout the course, participants will gain hands-on experience not only understanding the components of these systems but also learning how they can be used together to solve real business challenges. The course is taught by a former engineer and senior manager from Amazon and IMDb, offering a unique perspective on how to master the most popular data engineering technologies and integrate them with various distributed systems.
Participants will have the opportunity to work with a multitude of tools and frameworks including HDFS, MapReduce, Pig, Spark, Apache Flink, Hive, HBase, MongoDB, Cassandra, and Kafka, among many others. This empowers learners to manage big data on a cluster, design real-world systems, handle streaming data in real-time, and more. With over 25 different technologies covered in 14 hours of video lectures, those who complete the course will have a deep understanding of Hadoop and its associated distributed systems, ready to apply their new skills to real-world problems in various companies and industries.
This course excels in providing learners with a deep understanding of Hadoop and its associated distributed systems, as well as hands-on experience working with a multitude of tools and frameworks. It's ideal for those looking to manage big data on a cluster and design real-world systems.
This comprehensive course is designed to equip beginners, whether they are programmers or professionals from other fields, with an in-depth understanding of the tools essential for managing and analyzing big data. Through hands-on exercises, learners will become well-versed in Hadoop and Spark frameworks, which are among the most widely used platforms for processing large data sets in the industry. By the end of the course, students will gain the ability to describe key components and fundamental processes involved in Hadoop's architecture, software stack, and execution environment.
The coursework also delves into data science and guides learners in the application of critical concepts and techniques, such as Map-Reduce, which are employed to address basic challenges in big data analysis. As a result, students will develop the confidence to engage in meaningful discussions on big data and its analysis processes. This course serves as the perfect starting point for those seeking to expand their knowledge in the field of big data and take their career to new heights.
This course is ideal for learners who want to gain a strong understanding of Hadoop and Spark frameworks and develop proficiency in big data processing and analysis using tools like Hive, HBase, and MapReduce.
Understanding Hadoop is essential for working with big data, as it helps in the efficient processing and management of vast amounts of information. This course is designed to provide a comprehensive introduction to the key aspects of Hadoop, including its file systems, processing engine - MapReduce, and numerous libraries and programming tools. With the guidance of developer and big-data consultant Lynn Langit, learners will discover how to set up a Hadoop development environment, run and optimize MapReduce jobs, and develop essential skills for coding basic queries with Hive and Pig.
Beyond the core Hadoop components, this course also explores the extensive Apache Spark libraries that can be integrated with a Hadoop cluster, as well as options for executing machine learning jobs within the same environment. By delving into the wide array of libraries, tools, and techniques associated with Hadoop, participants will gain a solid foundation in the underlying principles of big data processing, enabling them to apply the latest advancements in data analytics effectively and confidently to their projects and workplace.
This course is ideal for gaining a solid foundation in Hadoop, including its file systems, processing engine - MapReduce, and numerous libraries and programming tools. Additionally, it covers the Apache Spark libraries that can be integrated with a Hadoop cluster.
This comprehensive course is designed to equip students with a strong understanding of big data, focusing specifically on Hadoop and Spark with Scala. By taking this course, students will develop the skills needed to switch careers or enhance their current work in the field of big data. Throughout this program, students will delve into various topics including Hadoop, HDFS, YARN, MapReduce, Python, Pig, Hive, Oozie, Sqoop, Flume, HBase, NoSQL, Spark, Spark SQL, and Spark Streaming.
Designed to be a one-stop resource, this course provides all necessary materials and programs, ensuring students have a smooth learning experience. The instructor is committed to offering support and assistance for any inquiries that may arise during the course. With a focus on both the basics and advanced aspects of the Hadoop eco-system, as well as Spark, this course is perfect for individuals looking to expand their knowledge and excel in the world of big data.
This course is ideal for those looking to excel in the world of big data, specifically focusing on acquiring expertise in Hadoop, Spark, and Scala, covering both basic and advanced aspects.
This comprehensive tutorial delves into the inner workings of Hadoop's data warehousing platform, Apache Hive. Participants will gain an understanding of the need for Hive architecture and its various configuration parameters. The course will cover how Hive fits in with Hadoop's ecosystem, as well as its architecture, installation, and configuration. By learning about Hive, the SQL of Hadoop (HQL), users will be able to work with SQL queries on data stored within the Hadoop ecosystem.
Throughout the course, students will be exposed to numerous Hive demonstrations, including creating databases, working with data types, creating and managing Hive tables and views, and understanding how different layers interact with each other within the Hive platform. Additionally, the course will explore the implementation of real-time projects, project setup, permissions, auditing, troubleshooting, and provide sample data and queries for practical replication. With multiple assessments strategically placed to test understanding, this course is an invaluable resource for those looking to expand their knowledge and skills in the Hadoop ecosystem and Hive.
This course is ideal for gaining proficiency in using Hive as a data warehousing tool within the Hadoop ecosystem and for mastering SQL queries on Hadoop-stored data.
The course focuses on the powerful combination of Apache Hadoop and Apache Spark to harness the full potential of big data analytics. As the importance of data-driven decision making continues to grow, the ability to effectively manage and analyze large datasets becomes increasingly valuable. This course provides an in-depth understanding of how to build scalable and optimized data analytics pipelines by utilizing the strengths of both Hadoop and Spark. Through the guidance of an experienced instructor, participants will explore topics such as data modeling and storage optimization on HDFS, as well as scalable data ingestion and extraction using Spark.
A hands-on approach is employed throughout the course as students are given the opportunity to work on a use case project, thereby allowing them to practice and apply the techniques they learn. With the skills developed in this course, students will be well-equipped to optimize data processing in Spark and unlock the true potential of big data in their professional endeavors. This course is ideal for data analysts, data engineers, and anyone seeking to enhance their data analysis capabilities by incorporating the powerful duo of Hadoop and Spark into their pipeline. As the demand for expertise in big data technologies continues to rise, mastering these tools can propel your career forward and create countless opportunities in the rapidly evolving field of data analytics.
This course is ideal for data analysts, data engineers, and anyone seeking to enhance their data analysis capabilities by incorporating the powerful combination of Hadoop and Spark into their data analytics pipeline for managing and analyzing large datasets.
The Learn Big Data: The Hadoop Ecosystem Masterclass is designed to help students master the Hadoop ecosystem using various tools and technologies, such as HDFS, MapReduce, Yarn, Pig, Hive, Kafka, HBase, Spark, Knox, Ranger, Ambari, and Zookeeper. This course is suitable for software engineers, database administrators, and system administrators who want to enhance their skills in Big Data, as well as other IT professionals who might need to do some extra research to understand certain concepts. Hadoop is one of the most sought-after skills in the IT industry, with the average salary in the US being $112,000 per year and up to $160,000 in San Francisco (source: Indeed).
This practical course offers more than 6 hours of lectures, covering both batch processing and real-time processing using the most popular software in the Big Data industry. By the end of the course, students will have a solid background in Hadoop ecosystem technologies, enabling them to engage in informed discussions with industry experts and update their LinkedIn profiles to attract recruiters from prestigious companies. The course provides support for students who might get stuck while trying to implement the exercises, with assistance available through message boards and a dedicated Facebook group for asking questions.
This course is ideal for mastering the various tools and technologies within the Hadoop ecosystem such as HDFS, MapReduce, Yarn, Pig, Hive, Kafka, HBase, Spark, Knox, Ranger, Ambari, and Zookeeper. By the end, students will have a solid understanding of these technologies and be able to engage in informed discussions with industry experts and update their LinkedIn profiles to attract top recruiters.
Explore the world of Big Data and Hadoop through a practical and hands-on approach. This course provides a detailed understanding of Hadoop and Big Data, specifically delving into real-world examples, Hadoop's ecosystem, its architecture, and how data is saved within the system. By the end of the course, learners will have a solid understanding of the reasons for Hadoop's development, various tools available, advantages of Big Data analysis, and why Big Data is in high demand.
Perfect for developers and testers, this course offers a comprehensive yet concise entry point into the Hadoop/Big Data domain. Not only does it provide an in-depth explanation of the Hadoop architecture, it also includes practical examples of handling files within HDFS and a demonstration of how MapReduce works with Hadoop. Aspiring big data professionals can rest assured knowing they have instructor support throughout and the opportunity to engage in a valuable learning experience at an affordable price.
This course is ideal for those seeking a practical understanding of Hadoop, its ecosystem, and Big Data analysis through real-world examples and hands-on experience. Suitable for developers and testers, the course offers comprehensive insight into Hadoop architecture and using tools like Hive and MapReduce for data processing.
Apache Hive is a data processing tool on Hadoop, allowing programmers to analyze large data sets. As an open-source software, it's a querying tool for Hadoop Distributed File System (HDFS), offering a syntax that is similar to SQL. This course takes students on a journey from basic Hive knowledge to advanced Hive concepts that are essential for working on real-time projects. Gaining expertise in advanced Hive topics will help prepare students for tackling big data and Hive projects.
This comprehensive course covers even the most intricate details of Hive, including basic and ADVANCE Hive concepts, as well as interview-asked use cases. Students will explore topics such as variables in Hive, table properties, custom input formatter, map and bucketed joins, advanced functions, compression techniques, configuration settings, working with multiple tables, and loading unstructured data. By the end, participants will be well-equipped to handle real-time Hive projects and challenges. The course also features a bonus section on frequently asked use cases in interviews, providing students with practical experience and a better understanding of Hive implementation in live projects. A step-by-step installation guide for Hadoop and Apache Hive is also available for download.
This course is best for gaining expertise in advanced Hive topics, equipping participants to handle real-time Hive projects and challenges in the Hadoop ecosystem. It also prepares students for tackling big data and Hive projects through comprehensive coverage of intricate details of Hive, including basic and advanced concepts.
“Big data" analysis is a hot and highly valuable skill – and this course will teach you two technologies fundamental to big data quickly: MapReduce and Hadoop. Ever wonder how Google manages to analyze the entire Internet on a continual basis? You'll learn those same techniques, using your own Windows system right at home.
Learn and master the art of framing data analysis problems as MapReduce problems through over 10 hands-on examples, and then scale them up to run on cloud computing services in this course. You'll be learning from an ex-engineer and senior manager from Amazon and IMDb.
This course is the best for learning how to frame data analysis problems as MapReduce problems using hands-on examples and scaling them up to run on cloud computing services.
This course delves into the world of Big Data by exploring the fundamentals of Hadoop and Spark, two key technologies that play a vital role in handling and processing massive amounts of data. Throughout the training, participants will gain hands-on experience working with Hadoop's distributed storage and processing capabilities, as well as understanding how the data warehouse system Hive can help streamline complex data queries. Similarly, Spark's powerful processing engine will be showcased, highlighting its ability to deliver rapid, reliable insights for both batch and real-time analytics.
Not only will learners acquire knowledge on the architecture and components that comprise Hadoop and Spark, but they will also develop an understanding of how to effectively leverage these tools in real-world applications. This includes understanding Resilient Distributed Datasets (RDDs), which allow for parallel processing across a cluster's nodes, offering improved efficiency and fault tolerance. By the end of this course, participants will have obtained the necessary foundation to confidently navigate the Big Data landscape and employ the various technologies at their disposal to gain valuable insights from massive data sets.
This course excels at introducing participants to the fundamentals of Hadoop and Spark, providing a solid foundation in Big Data processing technologies and real-world applications.
This comprehensive course offers an in-depth understanding of Apache Spark with Java, covering all the fundamentals necessary to develop Spark applications. By the end of the course, participants will have gained extensive knowledge about Apache Spark, big data analysis, and data manipulation skills, enabling them to help their companies adapt Apache Spark for building big data processing pipelines and data analytics applications. The course features 10+ hands-on big data examples, teaching valuable techniques to frame data analysis problems as Spark problems and imparting practical knowledge about the hadoop ecosystem.
The course is highly hands-on, geared towards providing real-life examples of developing Spark applications that participants can try on their own computers. Covering everything from Spark architecture and components, RDD, pair RDD, advanced Spark topics, to Spark SQL and running Spark in a cluster, this course aims to transform learners from 'zero to Spark hero' in just 4 hours. Whether you're looking to enhance your big data analysis skills or advance your career, this course is designed to equip you with the knowledge and expertise to develop robust Spark applications capable of analyzing gigabytes of data both on personal systems and in the cloud using Amazon's Elastic MapReduce service.
This course is perfect for those who want to gain extensive knowledge about Apache Spark, big data analysis, and data manipulation, enabling them to develop Spark applications as well as build big data processing pipelines and data analytics applications.
Embark on an exciting journey to learn Distributed Java Applications at Scale, Parallel Programming, Distributed Computing, and modern Cloud Software Architecture. With a focus on Java-based technologies, you will master the theory and practical skills needed to build distributed applications, parallel algorithms, and deploy groups of distributed Java applications on the Cloud. Along the way, you will also delve into Distributed Databases, scaling them to store petabytes of data, and building highly scalable and fault-tolerant distributed systems with technologies such as Apache Kafka, Apache Zookeeper, MongoDB, HAProxy, JSON, Java HTTP Server, and Client, Protocol Buffers, and the Google Cloud Platform.
Upon completing the course, you will be equipped with the expertise to apply best practices for building and architecting real-life distributed systems, scale your systems to handle billions of transactions per day, and deploy your distributed application on the Cloud. Moreover, you will be able to choose the right technologies for your use case and software architecture, and use modern Java-based techniques to store and handle large amounts of data. This course is ideal for those aiming to become a Software Architect or Technical Lead in the modern era, as most companies today run distributed systems and deploy them on the cloud. Master the world of distributed systems and cloud computing with Java and Hadoop, and be ready for the challenges of the modern tech landscape.
This course is ideal for mastering distributed Java applications at scale and learning modern cloud software architecture. It equips learners with the expertise to build and deploy distributed systems on the cloud using Java-based technologies.
Big Data is becoming increasingly important in today's world, as it enables organizations to gain valuable insights and make informed decisions. In order to harness the power of Big Data, you need to have a strong understanding of Hadoop, a popular open-source framework that allows for distributed storage and processing of large data sets. This course will provide you with a comprehensive introduction to Hadoop and the Cloudera CDH, which is a distribution of Hadoop and related tools specifically designed to help you manage your Big Data projects more effectively.
The course begins with an overview of the concepts of Big Data, Hadoop, and Cloudera, providing you with a solid foundation to build upon. You will then learn how to set up a Hadoop cluster using the Cloudera QuickStart VM, a virtual machine that offers a complete pre-configured Hadoop environment for easy and efficient learning. As the course progresses, you will discover how to create a Linux clean cluster with CentOS, and how to install and configure a cluster with the help of Cloudera Manager. By the time you complete the course, you will have gained the knowledge and skills necessary to successfully create and manage a Hadoop cluster, positioning you to unlock the full potential of Big Data in your organization or personal projects.
This course focuses on providing a comprehensive understanding of Hadoop and the Cloudera CDH, as well as offering practical guidance on setting up and managing Hadoop clusters effectively.
Processing billions of records requires a deep understanding of distributed computing, and Hadoop is an open-source framework designed to tackle this challenge. With Hadoop, you can efficiently handle large-scale data processing tasks using its powerful components: HDFS for storage, MapReduce for processing, and YARN for cluster management. This course, focused on the building blocks of Hadoop, aims to bridge the gap between programming and big data analysis, allowing you to effectively harness the power of Hadoop in your Java-based projects.
The course begins with a comprehensive overview of Hadoop's architecture, followed by hands-on lessons on setting up a pseudo-distributed Hadoop environment. You will gain practical experience in submitting and monitoring tasks within this environment. Towards the end of the course, you will explore various configuration options to ensure stability, reliability, and optimized task scheduling in your distributed system. By the end of this tutorial, you will have a solid understanding of the foundational elements of Hadoop, equipping you with the knowledge and skills to effectively utilize this powerful framework for tackling large-scale data processing challenges.
This course is ideal for learning the basics of Hadoop and Big Data analysis, as well as gaining a strong understanding of Hadoop's core components, which include HDFS, MapReduce, and YARN. Additionally, students will develop practical skills in setting up and managing a Hadoop environment using Java.
As the volume of data being stored today increases exponentially, traditional relational databases often struggle to keep up. Enter HBase, a database within the Hadoop ecosystem, specifically built to handle billions of rows of unstructured and semi-structured data with millions of fields. This course, Getting Started with HBase: The Hadoop Database, aims to help users become familiar and comfortable with utilizing HBase from the very beginning to ensure a seamless experience.
In this course, students will first learn how to design and layout data in a columnar format to optimize disk seeks and minimize read latency. They will then explore how to manipulate and access this data using the command line HBase shell and the HBase Java API. In addition, learners will be taught to process this data by performing complex aggregation and grouping operations using the MapReduce programming model in conjunction with HBase. By the end of the course, participants will be well-equipped to effectively manage huge volumes of data using the powerful capabilities offered by HBase.
This course is ideal for learning how to design, access, and process massive amounts of data using the HBase database within the Hadoop ecosystem.
Choosing the best Hadoop course is essential for learners who wish to enter the field of big data analysis and processing. With numerous courses available, it can be daunting to select the right one for your needs. To make an informed decision, consider the following criteria: understanding the basics of Hadoop and Big Data analysis, gaining hands-on experience in data processing, learning about the Hadoop platform and application framework, and mastering big data analytics with Hadoop and Apache Spark.
Learn how to use Hadoop for big data analytics and Apache Spark.
Get a practical understanding of the Hadoop Ecosystem and its tools like Hive and HBase.
Become proficient in using Hive for real-time usage in Hadoop querying.
Master the use of MapReduce and Hadoop for data analysis and processing.
Gain an understanding of how to create and manage Hadoop clusters using Cloudera CDH.
Become skilled in using Apache Spark with Java for big data analysis.
Understand distributed systems and cloud computing for big data processing using Java.
Ultimately, the ideal course should align with your learning objectives, require a reasonable time commitment, and feature positive reviews from previous students. Taking the time to research and compare courses will ensure that you make the best choice for your career growth in the Hadoop domain.
In conclusion, the comprehensive selection of Hadoop courses listed above is designed to cater to a wide range of skillsets and requirements, allowing you to build a strong foundation in Hadoop and Big Data analysis. Whether you are a beginner who wants to learn the basics, or an experienced professional seeking more advanced knowledge, these courses will provide you with the necessary skills and expertise to thrive in an ever-evolving industry.
As you embark on this journey, remember that the key to success is persistence, practice, and commitment. In time, you will gain hands-on experience and a thorough understanding of the Hadoop platform, its application framework, and associated tools. You will also master the art of harnessing the power of Hadoop and Big Data analytics for practical solutions. Don't hesitate to elevate your career to new heights by enrolling in one (or more) of these top-notch Hadoop courses today!
How much does a hadoop course cost?
The cost of a Hadoop course can vary greatly depending on the platform and the specific course chosen. Some courses are available for free on Udemy, while others can range from $14.99 to $63.90. On platforms like Coursera and LinkedIn Learning, you can also access courses through monthly or annual subscriptions, which can range from $19.99 to $59 per month.
How long do hadoop courses take?
The duration of Hadoop courses can also vary significantly, with some courses taking only a few hours to complete, while others may take multiple weeks. For example, some Udemy courses can be as short as 43 minutes or as long as 43 hours and 25 minutes. On platforms like Coursera or LinkedIn Learning, courses may span over several weeks with multiple hours of content per week.
What are different learning outcomes in Hadoop courses?
Different Hadoop courses focus on a variety of learning outcomes, such as mastering the Hadoop ecosystem and its components, working with Hadoop databases like HBase or Hive, learning to process and analyze big data using MapReduce and Spark, and understanding more advanced topics like distributed systems, cloud computing, and cluster management. Some courses may also include hands-on projects to help solidify your understanding of these concepts.
Prajnesh Eric Animilli (March 2, 2018). "Hadoop Starter Kit".
April 28, 2023