Hadoop is a highly scalable storage platform because it can store and distribute very large data sets across hundreds of inexpensive servers that operate in parallel. Large web companies such as Google and Facebook use Hadoop to store and manage their huge data sets.
Today, the world has become hyper-connected. People and organizations alike create massive volumes of data at ever-accelerating rates. As a result, big data analytics has become a powerful tool for businesses for analyzing this data. They convert this random, unstructured data into valuable data for profit and competitive advantage. In the midst of this big data rush, Hadoop, a cloud-based platform, has been heavily promoted as the one-size fits all solution for the business world's big data problems. Hadoop has proved to be a better solution compared with traditional databases.
Hadoop is not a database, but rather an open source software framework specifically built to handle large volumes of structured and semi-structured data.
Hadoop was designed for large distributed data processing that addresses every file in the database. This type of processing takes time. For tasks where fast performance isn't critical, such as running end-of-day reports to review daily transactions, scanning historical data, and performing analytics where a slower time-to-insight is acceptable, Hadoop is ideal. Hadoop scores over traditional databases in the aspects of normalization and scalability.
These are the key functionalities of Hadoop:
However, there are some limitations in Hadoop that need to be addressed:
- MapReduce programming is not a good match for all problems. It's good for simple information requests and problems that can be divided into independent units, but it's not efficient for iterative and interactive analytic tasks.
- Data security is another challenge that centers around fragmented data, even though new tools and technologies are surfacing. The Kerberos authentication protocol is a great step toward making Hadoop environments secure. Thus, steps must be implemented to make Hadoop secure.
- Hadoop does not have easy-to-use, full-feature tools for data management, data cleansing, governance, and metadata. Especially lacking are tools for data quality and standardization.
Hadoop has fundamentally changed the economics of storing and analyzing information. As recently as five years ago, a scalable relational database costed $100K per terabyte for a perpetual software license, plus $20K per year for maintenance and support. Today you can store, manage and analyze the same amount of information with a $1,200/year subscription. This difference in economics has attracted a lot of attention and will make Hadoop the centerpiece from which most large-scale data management activities and analyses will either integrate or originate. Earlier, we used to ask whether we could afford to store information. Today we ask whether we can afford to throw it away. This is not a technological argument; it is an economic one. Hadoop is part of the reason.
Initially, relational databases and Hadoop varied greatly in their capabilities. Hadoop required lots of hard coding and had a limited ecosystem of supporting tools, but with enough effort you could do amazing things with large volumes of data. But now, Hadoop has started to replace the relational database technology.
Now, with more robust SQL capabilities introduced in Hadoop infrastructure, the market has expanded itself tremendously. No longer is Hadoop just the domain of specialists. Sure, there are certain drawbacks of Hadoop. But, the functionalities which it provides scores over the limitations. Hadoop has become an evolving technology as it is an e open-source technology. Even enterprises invest more heavily in such technology. Thus, we can confidently say that Hadoop is moving forward to become a dominant technology.