The future of big data is very, very fast
A lot is being said about big data and its future possibilities. But, only two things can be said most certainly. One is surely the completely innovative infrastructure that it needs and the other is, it is going to be supremely fast. With the recent rise of real-time analytics engines and transactional databases like NoSQL, data processing will become lightning fast and the storage process will also match the speed. The development on both sectors drives the data-driven world to a different degree of abilities. Now, workload is no longer an issue since new heights are meant to be reached. In fact, in a search for the ultimate speed, a new generation of BI has come out. Data analysts can step back as machines will now handle business intelligence.
Little human intervention
Machine business intelligence, when arrives, will totally change the dimension of data-based workload. With a machine-level processing speed, human intervention is not needed at all. After machines coming into this, machines will analyze all the streamed data after a user has logged in and human intervention will only be a needless excess. After analyzing the data, the machine will display the relevant information from the business perspective. As huge amount of data pours in to populate tools like Spark and Hadoop, it is time to let machine understand the data better. Moreover, real-time databases that use NoSQL will become more popular since their response is immediate in terms of sending data for analysis. Thus, databases like MongoDB, Cassandra etc will become a growing need.
The other changes
As Spark’s introduction has changed the big data game altogether, gone are those days when batch-oriented data processing would occur. As analytics and transaction goes real-time hand in hand, the possibility of a connection between them becomes stronger. In fact, whatever is learnt from the processing and storage is included in a feedback loop that only the machine can process at that speed. Thus, each transaction will be more informative and contextual. In fact, if the dataset is highly connected, a graph database can speed up a million times more than the relational ones. Even after you increase the data size tremendously; it will perform way faster than the existing ones. The performance, in fact, is constant no matter what is the data type.
The new technologies
Tools are emerging from everywhere and databases are looking to get more connected. The recent development of the connector between Spark and Cassandra has boosted hopes of companies who want to extract business intelligence information from insane amounts of data at a terrific speed. In fact, new kinds of applications are now possible with so much speed at offering. In fact, a paradigm shift towards cloud is now evident and NoSQL has become a singular choice for many enterprises. NoSQL offers affordable solutions for situations like disaster recovery. Relational databases need trade-offs and since big data is going to be processed very, very fast, it is time that companies bunk these trade-offs and embrace the obvious option of NoSQL.
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