“A lot of information and a little space to store it “–All human civilizations had this common issue to sort out.
Big data Management has become one of the most sought after topics for discussion especially in IT sector. Companies, corporates and organizations are constantly struggling to find solutions to store data according to different classifications. Plethora of information gets accumulated every single day owing to the large number of transactions made in an organization, making Big Data Management a complicated Herculean task.
Big data management includes extracting both structured transactional information from the company in-house system as well as the non-structured data from the social networking sites like Facebook, Twitter and so on. Some firms even go to the extent of storing data what people do on their personal computers and mobile phones.
First and foremost, the main problem that the data ware housing teams come into clash is how to leverage prevailing technologies along with merging tools. Another grave issue most organizations deal with is, to figure out what data can bring out better business results and what information can be gotten rid of. Data stores of all the departments gets full due to the fact that most of the employees are reluctant to delete even a tiny piece of information from the system, out of the fear that they might be held responsible for the lost data or they may get fired later. But at the same time, they are very conscious about the cost involved and want to keep the storage sizes down.
Managing big volume of data of course require some serious thinking from the side of organizations having traditional data warehouses. They usually revolve around structured data, but much of what we call as “big data” is usually the un-structured or the semi-structured data. Conventional framework of data management really struggle under the massive heap of today’s data volume. However, the rapidly changing information technology is helping us to handle the enormous quantity of data. Now a days, the commonly used data analytical applications are NoSQL (commonly referred to ‘Not only SQL’), Hadoop , a software ecosystem and Map Reduce , which is a programming paradigm.
Taking present scenario into consideration, there is no doubt that the data volume is going to increase over the years by leaps and bounds, especially when mobile phones and other internet connected gadgets are on the rise. SQL is the present standard way to analyze data but later on, advanced technology like ‘ Spark’ will be popular. It is a processing engine developed to handle and accommodate large amount of data sets and analytics comprising of complex calculations (including machine language) which will be transacted at a very high speed. Introduction of advanced data technologies like Kafka and Spark can save a lot of money by storing large amounts of data and they also show you more efficient way of running a business.
There is no question that big data management has taken the business sector by storm and with advanced big data analytics on the prowl, companies will be busy molding more products to meet customer’s demand and expectations.