What is Big DATA  ?

DATA IS THE NEW GOLD 

What exactly is big data?

To really understand big data, it’s helpful to have some historical background. Here is Gartner’s definition, circa 2001 (which is still the go-to definition): Big data is data that contains greater variety arriving in increasing volumes and with ever-higher velocity. This is known as the three Vs.

Put simply, big data is larger, more complex data sets, especially from new data sources. These data sets are so voluminous that traditional data processing software just can’t manage them. But these massive volumes of data can be used to address business problems you wouldn’t have been able to tackle before.

The Three Vs of Big Data

Volume  : The measure of information matters. With huge information, you'll need to process high volumes of low-thickness, unstructured information. This can be information of obscure worth, for example, Twitter information channels, clickstreams on a page or a portable application, or sensor-empowered hardware. For certain associations, this may be many terabytes of information. For other people, it might be many petabytes.

Velocity   : Velocity is the quick rate at which information is gotten and (maybe) followed up on. Regularly, the most noteworthy speed of information streams legitimately into memory as opposed to being composed to plate. Some web empowered brilliant items work progressively or close to continuous and will require constant assessment and activity. 

Variety   : Variety alludes to the numerous kinds of information that are accessible. Customary information types were organized and fit flawlessly in a social database. With the ascent of huge information, information comes in new unstructured information types. Unstructured and semistructured information types, for example, content, sound, and video, need extra preprocessing to infer importance and help metadata.

The Value—and Truth—of Big Data

Two more Vs have emerged over the past few years: value and veracity.

Data has intrinsic value. But it’s of no use until that value is discovered. Equally important: How truthful is your data—and how much can you rely on it?

Today, big data has become capital. Think of some of the world’s biggest tech companies. A large part of the value they offer comes from their data, which they’re constantly analyzing to produce more efficiency and develop new products.

Recent technological breakthroughs have exponentially reduced the cost of data storage and compute, making it easier and less expensive to store more data than ever before. With an increased volume of big data now cheaper and more accessible, you can make more accurate and precise business decisions.

Finding value in big data isn’t only about analyzing it (which is a whole other benefit). It’s an entire discovery process that requires insightful analysts, business users, and executives who ask the right questions, recognize patterns, make informed assumptions, and predict behavior.

But how did we get here?

The History of Big Data

Although the concept of big data itself is relatively new, the origins of large data sets go back to the 1960s and '70s when the world of data was just getting started with the first data centers and the development of the relational database.

Around 2005, people began to realize just how much data users generated through Facebook, YouTube, and other online services. Hadoop (an open-source framework created specifically to store and analyze big data sets) was developed that same year. NoSQL also began to gain popularity during this time.

The development of open-source frameworks, such as Hadoop (and more recently, Spark) was essential for the growth of big data because they make big data easier to work with and cheaper to store. In the years since then, the volume of big data has skyrocketed. Users are still generating huge amounts of data—but it’s not just humans who are doing it.

With the advent of the Internet of Things (IoT), more objects and devices are connected to the internet, gathering data on customer usage patterns and product performance. The emergence of machine learning has produced still more data.

While big data has come far, its usefulness is only just beginning. Cloud computing has expanded big data possibilities even further. The cloud offers truly elastic scalability, where developers can simply spin up ad hoc clusters to test a subset of data.

Benefits of Big Data and Data Analytics:

• Big data makes it possible for you to gain more complete answers because you have more information.
• More complete answers mean more confidence in the data—which means a completely different approach to tackling problems.