Do you wonder how data in large applications like YouTube is managed? How is one video streamed to millions of devices at the same time? There is a format to process such data and a framework that helps in doing so.
The data processed and stored in large-scale applications is known as Big Data, and a framework called Hadoop helps facilitate this process. This article takes a look at what Hadoop is, how does it simplify the management of huge volumes of data, and how you can get started with learning it.
How to Learn Hadoop
Hadoop is a complex and detailed framework, so you must understand its purpose and features before learning its dynamics.
What is Hadoop?
Hadoop is an open-source framework developed by Apache software for distributed processing and handling of large datasets across multiple computing groups. Hadoop is used to help manage and store your application’s data across small-scale servers distributed across the globe.
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Hadoop reduces the load of data handling from one centralized server by breaking it down into smaller tasks. Multiple servers can then take up these tasks and independently complete them. This constructs a modular design of the infrastructure and helps to scale the system. This means that you can start your datastore from a very small set of distributed servers before increasing your total capacity based on the usage requirements of your application.
Features of Hadoop
Hadoop offers multiple features as a data management solution. Here are some of them:
Ability to Quickly Store and Process Large Amount of Data
Hadoop-based applications can efficiently process large amounts of data. Hadoop breaks down the processing to be carried out on the data across its server, which means that if a large file that is stored across multiple nodes needs to be manipulated, instead of bringing the parts of the file together to operate on it, the operation instructions are split across the servers to speed up the process.
Huge Computing Power
Since there are multiple servers to complete the task at hand, the overall capacity of the entire system increases. You can carry out huge, complex operations on stored data very easily by splitting the task between individual servers.
High Fault Tolerance
As Hadoop works on the principle of distributed processing across multiple nodes (i.e. servers), failure of a node does not disrupt the entire process. The tasks scheduled on the failed node can then be reassigned to another node, and the system continues to normally function. This makes sure that your application does not face any downtime, and helps in making it more reliable.
As far as data losses are concerned, Hadoop maintains multiple copies of the stored data to cover for any storage hardware failures.
Hadoop offers you the flexibility to work on your data as and when you want. With normal datastores, manipulating data is a complex and time-consuming task. This is why it is advised to process and clean data before storing it. But with Hadoop, you can easily run operations on stored data, which removes the obligation to pre-process data before storing.
As previously mentioned, data storage and processing happen via small, distributed servers called nodes. Hadoop based applications can easily begin with a small set of loads for small traffic in the beginning, and then increase the number of nodes as the application’s requirements scale.
What is Hadoop Used For?
Hadoop is growing as a data storage solution for modern applications. Before we set down to learn the framework, let’s explore some of its main use cases:
Low-Cost Data Storage
One of the biggest use-cases of Hadoop is in the form of a cost-effective data storage solution. The ability to begin with a small number of server nodes in the beginning, and scaling easily when the need arises makes Hadoop the go-to for any modern application right from the beginning.
Internet of Things
The IoT technology is known to generate a huge amount of data. This data further needs to be processed in real-time for IoT-based devices to be able to properly communicate with each other. Hadoop fits the scene perfectly as its robust, super-fast data storage and processing abilities help provide the IoT based devices the data that they need exactly in time.
Complementing Data Warehouses
Data warehouses are large collections of business data used to drive decisions. Warehouses are an organized store of data that is not accessed very often and is processed to help gain business-related insights into an application. Hadoop is starting to find its usage in parallel with Data Warehouses to store and process such data faster.
Hadoop is an advanced data distribution and processing system with diverse uses, so there is plenty to learn. Following is a list of resources to help you get started:
The Best Hadoop Resources
As Hadoop is an open-source framework, programmers have tried to create content that can help you easily get started. Let’s take a look at the free and paid video courses that are available for Hadoop:
Big Data & Hadoop Full Course by Edureka
- Platform: YouTube
- Duration: 10.5 hours
- Price: Free
- Prerequisites: None
- Start Date: On-Demand
This Edureka course is meant for an audience that’s new to Hadoop. It is freely available on YouTube as a long-duration video that covers many topics. Beginning from the installation of the dependencies and the basics of the Hadoop concept, this course takes you on a long detour of all features offered by the Hadoop framework. At the end of the course, there’s a section on the most popular Hadoop questions asked in interviews, which is a great way to top off a long journey of learning.
Hadoop Platform and Application Framework by UC San Diego
- Platform: Coursera
- Duration: About 26 hours
- Price: Free
- Prerequisites: None
- Start Date: On-Demand
Offered by UC San Diego, this course is a trusted resource to take your first steps into the world of Hadoop and distributed data management.
The course is divided over five weeks, two of which are aimed at giving you an introduction to the topic, with minimum exposure to complex concepts. In the third and fourth weeks, HDFS and MapReduce are introduced, keeping in mind not to overburden you with complexities. The final week aims at giving you a walkthrough of Spark, one of the competitors of MapReduce, and helping you understand the fundamentals behind the concept.
All in all, if you have stumbled across Hadoop for the first time, this course is the perfect anchor to your journey of learning.
The Ultimate Hands-On Hadoop by Sundog Education
- Platform: Udemy
- Duration: About 14.5 hours
- Price: $120
- Prerequisites: Basic understanding of the Linux environment, and some programming experience with Python or Scala
- Start Date: On-Demand
The Ultimate Hands-On Hadoop has a great series of interactive hands-on projects to help you begin in the world of distributed data management. The course offers a chapter on setting up the dependencies and then dives right into the Hadoop framework by beginning with HDFS and MapReduce — two of the core concepts in Hadoop.
Considering the pace of the content, this course is a great resource to take after you have gained a basic understanding of the technology. If you have prior experience with Hadoop, you can be assured that this course will perfect your skills and help you gain an understanding of the top-level Big Data management solution irrespective of your professional skills.
Apart from video courses, many books are available to help you get started with Hadoop. Some top ones include:
‘Hadoop for Dummies’ by Dirk deRoos
Priced about $22 as of writing, this book is a great asset for beginners in Hadoop. As the name suggests, Hadoop for Dummies is specifically tailored for beginners, with adequate attention on explaining the fundamentals before diving into the more advanced topics.
The book begins by explaining the origins of Hadoop, its purpose, and its benefits. It makes you comfortable with the entire idea of distributed computing before diving into the specifics of the Hadoop framework.
‘Hadoop: The Definitive Guide’ by Tom White
Priced about $30 on Amazon at the time of writing this article, this book aims to be among the most comprehensive books on Hadoop. It is considered as the “Hadoop Bible” and it will help you learn how to create and maintain scalable, reliable distributed systems with Hadoop. It is also a great asset for system administrators who are looking to set up Hadoop clusters.
This book is one of the best alternatives available for experienced programmers, with its clear writing style and detailed explanation. It serves as a one stop guide to set up your own Hadoop structure from scratch. Advanced concepts like MapReduce are explained from the very fundamentals, which helps build a solid understanding of how to build your own distributed application easily.
‘Hadoop in Practice’ by Alex Holmes
Priced about $40 at the moment (check the latest price here), this book is best for people with experience in distributed systems and are looking to build on it by learning Hadoop.
This book has over 85 examples in the form of questions and answers to help clear out your doubts on the subject. It also features a section on the various best practices that should be followed for BigData analysis.
Hadoop in Practice addresses intermediate topics like using Hadoop with Pig. The book is a great resource for people who have prior experience with MapReduce and HDFS in Hadoop.
Apart from the video courses and books, there are also a great number of tutorials available online. Here are some great pieces to begin with:
Hadoop Tutorial by Simplilearn
The Hadoop Tutorial by Simplilearn offers a series of beginner-friendly text and video resources to help you get started with the framework. The tutorial explains the fundamentals behind the subject before moving on to cover more complex topics like HDFS, YARN, and MapReduce. If you are looking for a one-stop solution for a text and video course to get started with Hadoop, your search ends here.
Hadoop Tutorial for Big Data Enthusiasts by DataFlair
With ample content on the history, features, setup as well as advanced use-cases, this course offers the perfect roadmap to mastering Hadoop. It is a great point to start at, as it is a text-based course supplemented by video examples. While this course starts from the basics, it does not dive deep into complex concepts.
The course focuses on building a strong understanding of the basics of a distributed system. You may want to couple this with a more advanced course to help solidify your understanding of the subject.
Hadoop Tutorial by TutorialsPoint.com
This resource is more of a glossary of subtopics of Hadoop. However, the volume of content on each subtopic is great, and this course can be an important asset to you once you are done with the basics and are looking for a resource you can refer to whenever you need help.
How Long Does it Take to Learn Hadoop?
Hadoop is a powerful concept for distributed data management, so expect to spend about three weeks to build a solid understanding. Rigorous practice, aided with example projects for a duration of another three weeks is adequate to unleash the full potential of this system.
If you are looking to learn Hadoop well enough to implement it on a professional scale, you can look forward to two to three months of regular learning and practice. Anything more than that will only perfect your Hadoop skills.
Should You Study Hadoop?
Having compiled a great collection of resources and tutorials on Hadoop, we now need to answer the most important question of all: Should you learn Hadoop?
The answer to this is simple: If you are willing to dive into the world of distributed systems, or if you have prior experience with designing systems and are looking to upgrade your skills, Hadoop is the best field to invest your time and resources into. Hadoop is a cost-effective, robust solution to storing and managing BigData, and is the perfect solution for growing applications.
If you are looking to make a career as a systems architect, Hadoop is one of the basic skills to be able to meet your job requirements. A solid foundation in Hadoop is a must before you set out to explore the dynamics of system design.
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