Big data is one of the most trending topics in the last two decades. It is due to the massive amount of data that has been produced as well as consumed by everyone across the globe. A major revolution in the internet during the past years led to this drastic amount of data generation.
We cannot simply specify Big Data as lots of data, it is much more than that. It is a way of providing opportunities to utilize new and existing data, and discovering new ways of capturing future data to really make a difference in business operations.
We know that Big data has large sets of raw data, and that data can be structured, semi-structured, or unstructured. It is difficult to be absolutely certain about a single source from which data is originating, it is collected from a variety of sources, ranging from business transactions, pictures, videos, search engines, social media, websites, apps, and much more. This information is gathered, recorded, stored, and analyzed with the purpose of getting meaningful insights that will help the organization grow.
While big data holds a lot of promise, it has its own challenges. It’s not enough to just store the data. It requires clean data, or data that are organized in a way that enables meaningful analysis requires a lot of work. Data scientists spend 50 to 80 percent of their time curating and preparing data before it can actually be used.
Big data technology is changing at a rapid pace. A few years ago, Apache Hadoop was the popular technology used to handle big data, latter Apache Spark was introduced. Today, a combination of the two frameworks appears to be the market leader.
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