What’s Big Data And What Should Be Your Strategy To Control It?
Big data is the new topic now. You can see it being discussed on the internet and even news on TV. As a matter of fact, big data is a huge concern for today’s business owners. The technological developments have resulted in the invention of thousands of devices that are collecting data 24/7 all around us. Look at computers, website forms, social networking websites, CCTV cameras, barcode scanners etc. All these things are examples of devices collecting data on a daily basis. Handling such huge amounts of data is a big challenge for the big enterprises today.
Understanding Big Data
A general understanding of big data is that it is so huge that the commonly used softwares for data management and handling are not suitable to handle it or simply can’t process this data. The best way to define big data is with the help of 3 Vs: volume, variety and velocity. Volume defines the hugeness of the data i.e. how big it is and how much it is. Variety defines the many forms and formats of data that we collect i.e. audio, video, text, images etc. Velocity is the speed at which the data is being collected through various platforms. Think of millions of videos being uploaded on Youtube every week and billions of pictures uploaded on social networking platforms every month.
Big Data Platforms And How They Work
Of course, large enterprises must process the big data they collect on daily basis and make use of it. To do this they need the software and hardware that can take the weight of mountains of data. Hadoop is currently the most popular platform for handling and managing big data. It is also one of the most unique softwares around for working on big data. It lets huge businesses store huge amounts of data even if the storage capacity of their servers is lower than the data they are collecting. It arranges the data, allocates data the right storage space logically, allows companies to work on that data in real time and much more.
The way Hadoop works is that it consumes the power of many servers that are not sharing their memories with each other. The software is running separately on these servers but these servers are connected to each other through a network. You feed huge data to the software and from there it is its job to break this data into pieces and feed these pieces into the memories of numerous servers it is running on. It creates copy stores of the data on all servers so deletion of data or a hazard with any of your servers doesn’t result in the loss of your precious data.
When you work with Hadoop it handles your commands in the fittest possible way by using the power of several processors working on each server. When you work on the data Hadoop manages the operations and lets every server (or processor) do only the amount of work that it needs to do on the data it is handling – the rest is handled by other servers. The framework at work here is MapReduce. The way this framework works is that it first stores the big data in small shreds by breaking it into pieces and when you send a query to the network it collects the data stored in various locations, processes it and presents the out to your query. In short, many different tasks are being performed simultaneously without the load going on just one processor.
The Problems Posed By Big Data And Their Solution
When your data is extremely huge in size and it is so complexly arranged that you can’t view it on the conventional tables, you will need a software to help you here. You mostly need to process such huge data during analysis to find various trends or certain combinations of datasets. A good example is your online store where you want to present the products your customers are most interested based on their search history, internet surfing or answers given to certain questions. Hadoop has been designed to handle such big data issues and the most important thing to know here is that Google manages its huge data with Hadoop.