Big Data is undoubtedly a popular field.
And in your learning journey, you’ll come across many solutions and technologies. The most important one among them would probably be Apache Hadoop. In our introduction to Hadoop, you’ll find answers to many popular questions such as:
“What is Hadoop?”
“What are the features of Hadoop?”
“How does it work?”
Let’s dig in.
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What is Hadoop?
Hadoop is an open-source framework which is quite popular in the big data industry. Due to hadoop’s future scope, versatility and functionality, it has become a must-have for every data scientist.
In simple words, Hadoop is a collection of tools that lets you store big data in a readily accessible and distributed environment. It enables you to process the data parallelly.
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How Hadoop was Created
Yahoo created Hadoop in the year 2006, and it started using this technology by 2007. It was given to the Apache Software Foundation in 2008. However, several developments took place, which helped the creation of this robust framework.
In 2003, Doug Cutting had launched a project called Nutch. Nutch was created to handle the indexing of numerous web pages and billions of online search.
Later in that year, Google released the Google File System. A few months later, Google released MapReduce. Read more about Apache spark vs MapReduce
Yahoo was able to create Hadoop based on these technologies. Hadoop increased the speed of data processing by letting users store data in multiple small devices instead of a big one.
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The thing is, the size of data storage devices was getting bigger. And processing data in those devices was becoming time-consuming and painful. The creators of Hadoop realized that by keeping the data in multiple small appliances, they could process it parallelly and increase the efficiency of the system considerably.
With Hadoop, you can store and process data without worrying about buying a large and expensive data storage unit. On a side note, Hadoop gets its name from an elephant toy. The toy belonged to the son of one of the creators of the software.
Introduction to Hadoop’s Components
Hadoop is an extensive framework. It has many components that help you in storing and processing data.
However, primarily it is divided into two sections:
- HDFS stands for Hadoop Distributed File System
The former is for storing the data while the latter is for processing the same. Hadoop might seem simple, but it takes a little effort to master it. Hadoop lets you store data in various clusters. The data could be of any format.
As it is open-source software, you can use it for free. Apart from that, Hadoop consists of many big data tools that help you perform your tasks faster. In addition to the two sections of Hadoop we mentioned above, it also has Hadoop Common and Hadoop MapReduce.
While they are not as significant as the previous two sections, they are still quite substantial.
Let’s break down each section of Hadoop for your better understanding:
The Hadoop Distributed File System lets you store data in readily accessible forms. It saves your data in multiple nodes, which means it distributes the data.
HDFS has a master node and slave nodes. The master node is called Namenode, while the slave nodes are called Datanodes. The Namenode stores the metadata of the data you store, such as the location of the stored block, which data block is replicated, etc.
It manages and organizes the DataNodes. Your actual data is stored in the DataNodes.
So, if HDFS is an office, NameNode is the manager and DataNodes are the workers. HDFS stores your data in multiple interconnected devices. You can set up the master nodes and the slaves nodes on the cloud as well as in the office.
YARN is the acronym for ‘Yet Another Resource Negotiator’. It is a significant operation system and finds applications in Big Data processes.
It’s the job scheduling and resource managing technology. Before YARN, the job tracker had to handle the resource management layer as well as the processing layer separately.
Most people don’t use the full name of this technology as it’s just a little humour. YARN can allot resources to a particular application according to its need as its resource manager. It also has node-level agents, which are tasked with monitoring the various processing operations.
YARN allows for multiple scheduling methods. This feature makes YARN a fantastic solution as the previous solution for scheduling tasks didn’t provide any options to the user. You can reserve some cluster sources for specific processing jobs. Apart from that, it enables you to put a limit to the number of resources a user can reserve.
MapReduce is another powerful tool present in the Apache Hadoop collection. Its main job is to identify data and convert it into a suitable format for data processing.
It has two sections: Map and Reduce (thus the name MapReduce). The first section identifies the data and puts it into chunks for parallel processing. The second section summarizes the entire input data.
MapReduce can execute any failed projects too. It splits a job into tasks where it first performs mapping, then shuffling and finally reducing. MapReduce is a popular Hadoop solution, and because of its features, it has become a staple name in the industry.
It can work in several programming languages such as Python and Java. You’ll be using this tool multiple times as a Big Data professional.
Hadoop Common is a collection of free tools and software for Hadoop users. It’s a library of incredible tools that can make your job easier and more efficient.
The tools present in Hadoop Common are in Java. The tools enable your operating system to read the data present in the Hadoop file system.
Another common name for Hadoop Common is Hadoop Core.
These four are the most prominent tools and frameworks in Apache Hadoop. It has plenty of other solutions for your Big Data needs, but chances are, you’ll be using only a few of them. Read more about Hadoop Tools.
On the other hand, it’s quite probable that you’ll need to use all four of these for any project you work on. It’s certainly a prominent big data solution.
Big Data Problems Solved by Hadoop
When you’re working with a vast amount of data, you face several challenges too. As the number of your data increases, your data storage needs will also rise. Hadoop solves many problems in this regard.
Let’s discuss them in detail
Storage of Data
Big data deals with vast quantities of data. And storing such vast amounts through conventional methods is quite impractical.
In the conventional method, you’ll need to rely on one big storage system, which is very expensive. Moreover, as you’ll be dealing with big data, your storage requirements will keep on increasing as well. With Hadoop, you don’t need to worry in this regard because you can store your data in a distributed fashion.
Hadoop stores your data in the form of blocks across its multiple DataNodes. You have the option to determine the size of these blocks. For example, if you have 256 MB of data and you have chosen to keep your data blocks of 64 MB, you’ll have a total of 4 different ones.
Hadoop, through HDFS, will store these blocks in its DataNodes. Its distributed storage facilitates scaling as well. Hadoop supports horizontal scaling.
You can add new nodes for storing data or scale up the resources of your current DataNodes. With Hadoop, you don’t need one extensive system to store data. You can use multiple small storage systems for this purpose.
These days, data is present in various forms. Videos, texts, names, audios, images, and many other formats are available in the market. And a company may need to store multiple formats of data. Primarily, data is divided into three forms:
- Data which you can save, access and process in a fixed format are called structured data.
- Data that has an unknown structure or form is termed as unstructured data. A file containing a combination of text, images, and videos can be an example of unstructured data.
- This form of data contains both structured and semi-structured kinds of data.
You might need to deal with all these formats of data. So, you’ll need a storage system which can keep multiple data formats as well. Hadoop doesn’t have pre-dumping schema validation. And once you’ve written a particular piece of data in Hadoop, you can reread it.
Hadoop’s ability to store heterogeneous data is another big reason why it’s the preferred choice for many organizations.
Access and Process Speed
Apart from storing the data, another major problem is of accessing and processing it. With traditional storage systems, it takes a lot of time to obtain a specific piece of data. Even if you add more hard disk space, it won’t increase the access speed accordingly. And that can cause a lot of delays.
For processing 1 TB data with a device having one 100 Mbps I/O channel, it’ll take around 3 hours to complete the process. On the other hand, if you four different devices, the process will complete within an hour.
Accessing speed is an essential part of big data. The longer it’ll take you to access and process the data, the more of your time will be spent waiting.
In Hadoop, MapReduce sends the processing logic to the multiple slave nodes. This way, the data stored in the slave nodes is processed parallelly. Once the entire data is processed, the slave nodes send the result to the master node, which combines those results and gives the summary to you (the client).
Because the entire process takes place parallelly, a lot of time is saved. Hadoop solves many problems faced by prominent data professionals. However, it is not the only data storage solution out there.
While Hadoop is an open-source framework which enables horizontal scaling, Relational Database Management Systems are another solution which will allow vertical scaling. They both are widely accessible, and if you want to learn big data, you should be familiar with them.
Hadoop is highly popular among Fortune 500 companies. That’s because of its Big Data analytics capabilities. Now that you know why it was created and what its components are, let’s focus on the features Hadoop has.
Big Data Analytics
Hadoop was created for Big Data analytics. It can handle vast amounts of data and process them in a small amount of time. It lets you store vast quantities of data without hindering the efficiency of your storage system.
Hadoop stores your data in clusters, and it processes them parallelly. Because it transfers logic to the working nodes, it’s able to use less network bandwidth. Through its parallel processing of data, it saves you a lot of time and energy.
Another advantage of using Hadoop is its cost-effectiveness. Companies can save a fortune in data storage devices by using Hadoop instead of conventional technologies.
Conventional storage systems require businesses and organizations to use a single and giant data storage unit. Like we’ve discussed earlier, this method isn’t much use because it isn’t sustainable for handling Big Data projects. It is highly expensive, and it costs keep increasing as the data requirements rise.
On the other hand, Hadoop reduces the operating costs by letting you use commodity storage devices. This means you can use multiple inexpensive and straightforward data storage units instead of one giant and expensive storage system.
Running a large data storage unit costs a lot of money. Upgrading the same is expensive too. With Hadoop, you can use fewer data storage units and upgrade them for less cost as well. Hadoop also enhances the efficiency of your operation. All in all, it’s an excellent solution for any enterprise.
Data requirements for any organization can increase with time. For example, the number of accounts on Facebook is always growing. As the data requirements for an organization rise, it needs to scale its data storage further.
Hadoop provides secure options for more data scaling. It has clusters which you can scale to a large extent through adding more cluster nodes. By adding more nodes, you can easily enhance the capability of your Hadoop system.
Moreover, you wouldn’t need to modify the application logic for scaling the system.
Hadoop’s environment replicates all pieces of data stored in its nodes. So if a particular node fails and loses the data, there are nodes to back it up. It prevents data loss and lets you work freely without worrying about the same. You can process the data irrespective of the node failure and continue your project.
Hadoop has plenty of Big Data solutions which make it very easy for any professional to work with it. The geniuses at Apache have put in a lot of effort into making Hadoop a fantastic Big Data solution.
Hadoop’s commercial solution called Cloudera can help you with many avenues of Big Data. It can also simplify working with Hadoop as it helps you with running, optimizing, installing, and configuring Hadoop for your requirements.
Hadoop Common has plenty of tools that make your job easier. As Hadoop is an Apache product, it has a beneficial community of other professionals who are always ready to help. It gets regular updates which enhance its performance too.
With so many advantages, Hadoop quickly becomes the favourite for any Big Data pro. Hadoop finds uses in many industries because of its versatility and functionality. If you are interested to learn more about Hadoop, check out our Hadoop tutorial.
Let us discuss some of its prominent use cases so you can understand its applications.
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Hadoop Use Cases
As Hadoop is a prominent Big Data solution, any industry which uses Big Data technologies would be using this solution. There are plenty of examples of Hadoop’s applications.
Corporations of multiple sectors also realize the importance of Big Data. They have large volumes of data, which they need to process. And that’s why they use Hadoop and other Big Data solutions.
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From a considerable amount of employee data to a long list of consumer numbers, the data could be of any form. And like we’ve discussed earlier, Hadoop is a robust data storage framework which facilitates fast data access and processing of the same.
There are many examples of Hadoop use cases, some of which are discussed below:
Facebook and other social media platforms store user data and process them through multiple technologies (such as Machine Learning).
From videos to user profiles, they need to store a large variety of data which they can through Hadoop.
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Hospitals employ Hadoop to store the medical records of their patients. It can save them plenty of time and resources by storing the data in a more easily accessible platform.
By storing the patients’ claims data in a more accessible platform (Hadoop), they can manage these records better.
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What are the significant differences between Hadoop 1 and Hadoop 2?
The components of Hadoop 1 use MapReduce whereas Hadoop 2 has YARN and MapReduce version 2. Hadoop 1 uses Hadoop Distributed File System for storage purposes whereas Hadoop 2 uses HDFS for storage and YARN on top of it. In terms of architecture, Hadoop 1 uses a master-slave architecture with a single master and multiple slaves. Hadoop 2 also follows master-slave architecture, but the difference here is it has a lot of masters and multiple slaves too instead of one.
What is the difference between Teradata and Hadoop?
Hadoop is an open-source programming framework that operates on huge chunks of data and is majorly used to perform computation. Teradata, on the other hand, uses large data house operations. Hadoop follows master-slave architecture whereas Teradata is a massively parallel processing system. Furthermore, Hadoop uses Big Data technology but Teradata is built on RDBMS and uses a fully scalable and functional database warehouse. HDFS, MapReduce, and YARN are three components that Hadoop architecture is made up of. Teradata is made of BYNET, AMPs, and Parsing Engine. Teradata is a commercial database whereas Hadoop is an open-source framework.
How is Hadoop cost-effective?
Due to Hadoop operating on cost-effective hardware, its models are automatically cost-efficient. This is different from how it operated earlier. The traditional databases used expensive hardware and worked with high-end processors mostly to accommodate Big Data. The biggest issue that relational databases go through is finding enough capacity to store data. Sometimes, this could lure a lot of costs and can compromise the budget. This has resulted in removing the raw data from the premises. Since Hadoop is open-source, it is totally free to use, and that’s how Hadoop drives towards cost-effectiveness, and secondly, it uses commodity hardware which is pretty cheap to work with. All of this saves ample cost for Hadoop making it cost-effective.