Big Data Solutions - Architecture, Infrastructure and Analytics | W3schools

With rapid advancement in technology, something new is created every day. Whether it is a new gadget or new software, it always manages to make everyone’s lives more convenient and definitely easier. Big Data Solution is one such advancement in the technological world that has benefitted the corporate world in many ways. It is often considered to be a blessing in disguise and has proven to be of great help.

Also Read: HDFS

To understand the concept of Hadoop and Big Data Solution everyone first needs to understand what is Big Data and how is it different from the normal data recorded by any company. When the volume data is massive and cannot be recorded using the traditional methods, or is very cumbersome and tiring, then it can be considered as big data. It may cover more than one subject and may involve more than one technique or tool. Hence, Big Data is large sets of data consolidated into a pack, and the aforesaid solution is a method of processing this data effortlessly.

Hadoop is the most convenient solution to all the problems a person faces when dealing with big data. There are not many software available in the market, except for Hadoop, which solves this issue. Hadoop uses the MapReduce algorithm, originally developed by Google, which has been modified by them in order to increase its efficiency. The data is run and processed through multiple CPUs connected to the Hadoop network, as a result of which the software is able to return a result for the analysis at a shorter time when compared to other software.

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What was the old approach?

In the traditional approach, the companies and firms would store the data on a relational database management system using their computer and the most basic methods available to the end users. Such database management systems would then gradually process the data and record it in the various fields provided. The systems would be programmed in such a manner that they could be accessed from anywhere and would provide the most accurate results to the users for further analysis.

What was the problem?

The underlying problem in the traditional method was the volume of the data being processed. If the data was in massive volumes, then the system would hang and get stuck at times. This caused a huge limitation for the big enterprises that had a vast client base and required to process data instantly. The standard servers used by these database management systems were not equipped to handle such great volumes of data at one time due to which it caused such limitations.

What was Google thinking about this?

With the emergence of the Big Data Problem, Google came up with a revolutionary solution. This solution was an algorithm created by the experts present at Google called “MapReduce”. This one algorithm had the power to redistribute the work of one server to many computers attached to the central network. Such redistribution would increase the capacity of the database management system and would also provide speed in processing a large amount of data. The algorithm also ensured to collect results from the different computers at the end to present the complete dataset on the central system. MapReduce basically uses the concept of branching out and sharing the load.

What is Hadoop?

Once Google came up with this great solution to the Big Data Problem, Doug Cutting and his team undertook a project named Hadoop to establish a top-notch program for Big data solution. This was an open source project and is entirely based on the MapReduce algorithm. The project that started in 2005 has now developed into world-famous software that uses the aforesaid algorithm to process large volumes of data parallelly on numerous CPU nodes. It runs on various clusters of PCs and has the capacity to process ‘Big Data’ instantly and effortlessly. It is a one-stop solution for all those huge corporate firms looking for a single server equipped with distributed storage and centralized access. Hadoop offers all these in single programming models and is designed dexterously to connect a single server to hundreds of computers at one go.

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How Hadoop is helping by providing Big Data Solutions

•   Low-Cost Storage– The cost of Hadoop is pretty modest, and given its ability in managing and storing huge data, the cost of data storage for a company reduces significantly by using the Hadoop framework.

•  Data Lake– The main purpose of the company is dealing with big data. Therefore, Hadoop allows companies to store huge amount of data in the original format. It also helps the companies in accessing their data in its raw form.

•  Data Integration– The Hadoop framework connects to all the computers in its network. Therefore, it allows one to access the data from any computer connected to the Hadoop network. After processing the data, one can even store the processed information in the open-source network, enabling others to access the processed data.

What is the framework for this application?

The Hadoop programme has four main layers that one needs to know about. The four most important aspects of this programme are:

•  Hadoop YARN – this layer deals with the coordination of various networks. It enables the single server to attach to cluster networks and distribute the workload. It ensures smooth processing of the data and efficient connection between cluster networks.

• Hadoop MapReduce – this is the algorithm used to plan out the entire programme. This algorithm is based on the solution derived by Google. It allows the data to be processed at different points parallelly. 

•  Hadoop COMMON – this is the encrypted Java files that help in storing the final results at one place. Such programming ensures that no one can get unauthorized access to the data files and that the end user can easily and accurately analyze the required data.

•  Hadoop Distributed File System – this distributed file system enables the working of the three above-mentioned parts of the Hadoop structure. It can be considered the glue holding them together.

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This efficient application, Apache Hadoop is a great boon for the large firms and is known to provide high standard applications prepared using the latest technology. Besides being an openly sourced network, it also uses Java files which makes it extremely compatible. Since Hadoop is java based, it is very easy for the users to use the software. A person with even a little prior knowledge of Java will be able to use it efficiently.

Hadoop knows it has come on the market recently. Hence not many people will be knowing how to use it. So, they provide tutorials on how to use Hadoop and the Hadoop Distributed File System. These tutorials are extremely helpful and informative. Due to these tutorials, many people have benefitted, such as Analytical Professionals, Software Professionals, and ETL developers. There are not many software available in the market which can handle large data in the manner Hadoop does. Hadoop is extremely efficient and very quick in returning responses. Projects like Hadoop has made the task of managing large volumes of data effortless and easy going. Hadoop aims at becoming the go-to software for every company when they think of big data storage and big data analysis.