Big data is all about volume, since you have to manage petabytes of data all the time. With so much data, you need to learn to manage the storage problem. You have to store the data, segregate the useful one and then analyze to get the results. While the useful bit may not be huge, you need to store the data first. Hence, data lakes have arrived to take the stupendous amount of unstructured data that arrives at a data centre from varied sources.
With Internet of Things becoming a pervasive reality, the possibility of creating new storage options grows even more. The more data there is, the more you know about things and the sharper your decisions become. Data lakes are worthier alternatives than a traditional data warehousing technique since here, data is in its unstructured, natural form so that data can be taken up or kept there for future examination.
Helping in analytics
Since big data requires new ways of data management, Data Lake brings with it new methods of data storage where you keep data from all sorts of sources without intervening unless required. Letting data be in its raw form, it opens up a new mode of approach called the bottom-up approach. So, once the various streams of data arrive including log data, structured data, unstructured data, sensor data etc. analytics can start from here. So, you need not venture into sorting or segregating data.
Since Data Lake does not sort data, it powers analytics to open up unknown possibilities between seemingly unrelated sets of data. For example, between customer behaviour and weather pattern, there can be a correlation coefficient which can now be derived using analytics. While there is lot to be done, there is no doubt that Data Lake is where it begins.
Benefits of Data Lake
Analytics is the sole reason why a new kind of architecture became necessary. Traditional ones didn’t work well and here was Data Lake, solving the big problem in a minute. Since data lies there just like that in Data Lake, you can pick up at ease and it becomes much faster since you don’t need to untangle the data once more.
Moreover, data has a new level of convergence and data mixing is easier than ever because of this characteristics. Hadoop is the greatest proof of its utility. Since big data demands constant decentralization where instead of data centre, data must move towards the edge where the applications reside so that the movement becomes faster and networks don’t hinder the speed. Data Lake also facilitates that.
Also, Data Lake offers insurmountable level of scalability since you can flex your data capacity on the go and save huge amount of resources. However, Data Lakes have to be designed properly to ensure that that it does not become a swamp. Like a good lake, it needs cleaning too. You need to focus what kind of answers you want and then populate the lake with the right kind of data.