The “Big” Trends in Big Data Analytics

Big data is the buzz word around the complete IT sector and has promising capabilities but the truth is that the tools for this technology are still under development phase and are emerging and evolving every day. With all the development undergoing it is still too risky to depend on Big Data completely for all the business needs. In fact, it is a two way sword either you take the risk of trusting the technology or risk your company’s existence.

The “Big” Trends in Big Data Analytics

Things are no longer as they used to be. For example, in past emerging technologies take years to fully develop but it is no longer the case as people present the solution in the matter of months. Similar is the case with the Big Data and there are emerging technologies which must be on your watch list and the list is given below.

• Big Data in the cloud

Hadoop, an open source framework and set of tools for processing the large volumes of data was originally designed to work on physical machines. But, this has changed and all thanks to the cloud computing. Many new technologies are available for processing the data in the cloud and some prominent examples are: Google’s Big Query data analytics, IBM’s Bluemix cloud platform and many more. The future of the big data lies in the cloud and not on machines.

• Hadoop is the New Enterprise Data Operating System

Map reduce is slowly evolving into the distributed resource manager along with other distributed analytical frameworks which in turn are turning the Hadoop into general purpose data processing system. With these new general purpose systems one can easily perform the many data manipulations and operations by plugging them into Hadoop as distributed file storage system.

• Big Data Lake

Big data lake defies the traditional approach of the design. Instead it the traditional approach and turns it completely upside down and then define the design principle as: “Take all the data and dump it into the big repository of the Hadoop without designing the data model beforehand. It is actually an incremental model for building the database.

• Predictive analytics

While analyzing the big data, it is not only the large amount of data but also the processing power which is required to handle such large volumes of data and attributes. Such a combination of large amount of data with the increased processing power allows analysts to explore the new behavioral patterns such as websites visited or locations.

• SQL rides Hadoop

One can practically drop the data in Hadoop and do the analysis on anything which seems like a boon but it is also a problem in itself because people need structure similar to something they already understand like SQL. Tools which allow SQL like querying let the people apply similar techniques to the data.

• Learning in depth

Deep learning is based on the neural networking, which is still evolving but has a great potential for solving the business problems. Deep learning enables the computer to identify the items of interest and deduce the relationships without the need of the specific models and programming instructions.

Big data is here and the technologies and trends are evolving by the minute. IT organizations are required to create the conditions allowing the analysts and data scientists to experiment so that one of the technologies can be integrated into the company.