Big Data Technologies are evolving every day, and Hadoop is one of them. What exactly is Hadoop, you ask? Hadoop is an open-source framework and it is used to store data and run applications on the clusters of commodity hardware. It’s kind of like a storage facility for any kind of data. It provides high processing power and the ability to handle a continuously increasing number of tasks.
History of Hadoop in Data Processing
The World Wide Web, around the late 1900s and early 2000s, grew. To help people locate the relevant information they were looking for, as well as the text-based content, search engines like Yahoo, Bing, Google came into existence. Earlier, humans delivered the content. However, eventually, automation was necessary as the web grew to millions of pages. Eventually, Yahoo released Hadoop as an open-source network. The project was based on distributed computing and processing. Today, Apache Sofware Foundation (ASF) maintains and manages Hadoop Technology. It is a global community of software developers and contributors.
Importance of Hadoop
Ability to store and process large amounts of varieties of data, at a high speed – As the volume of the data and its variety is increasing every day, especially from social media and IoT, it’s an advantage.
Computing Power – Hadoop works on a distributed computing model, and it processes data fast. In other words, the more computing there is, the more processing power we have.
Tolerance of fault – The processing and application are protected against any kind of hardware failure. If something goes down, the technology automatically assigns the task to other nodes, to make sure that the computing does not fail. It makes sure to make multiple copies of all data.
Flexible – Hadoop does not preprocess Big Data before storing it. You can store as much amount of data as you want for later use. That includes all the unstructured data like images, videos, and pictures.
Low Cost – The open-source network of this technology is free to use, and it uses commodity hardware to store large amounts of data. Above all, small businesses and industries can now start using Big Data technology with a minimal amount of investment.
Scalable – It is easy to grow the system and the network by adding more nodes. A large administration is not needed.
Challenges of using Hadoop
Not a match for all problems – It is good and efficient for simple requests and problems that can be divided into independent tasks. However, it is not the same for requests that need interactive analytics. It is file-oriented. The nodes in the network do not usually inter-communicate, it requires multiple shuffles and sorts. This results in the creation of multiple files and it is inefficient.
Talent Gap – It is significantly difficult for entry-level programmers to have relevant Java skills to be productive in this technology. However, the providers are now rushing to put SQL technology on top of Hadoop, since it is much easier to find programmers with SQL skills.
Data Security – Security issues have always tormented the use of Data analyzing technology; even though new technologies are continuously emerging. However, the Kerberos authentication protocol is a good step towards making the Hadoop technology of Big Data secure.
Data Management – Hadoop is not easy to use. The use of it requires full-feature tools for data management, data cleansing, metadata, and governance. There should be continuous maintenance of Data quality and standardization.
