What are the consequences of the clash between big data and cloud?
Today, when streaming data is available in huge quantities, the need for improving processing of data must be given importance rather than sending immense quantities of data through sorting process which becomes time consuming. And this ‘transporting’ of data from one place to another, which on paper looks pretty convincing, creates latencies as the humungous amounts of streaming data can overload the processing of Big data. Therefore CoreIT believes inbuilding a process that can be closer locally and is customized as per business goals must be implemented.
The actual issue
So the actual issue can be paraphrased as –the exponential growth of the amount of data on one hand and the world investing aggressively on cloud-strategies puts all kind of workloads on the cloud thereby creating latencies. Thus the clash between cloud and big data arises as most of the industries rely on this aspect to achieve productivity and seamless.
This usually implies that the application architecture level will change on a global level with huge amounts of data sets becoming a point for creating logic that make them meaningful and increases the efficiency of the entire system.
How to mitigate the clash between the core elements?
- To process data locally by bringing the same level of computing power thatis used in big data analytics is one way.
- Another option would be to use datacenters as colocation with optimized internal traffic capabilities from cloud providers and users in on-premise systems can speed up data processing and aggregation locally.
- The colocation pattern will also solve issues of bandwidth and latency arising from growth of data also.
Even distribution of workloads to a host of different cloud providers can be risky and therefore CoreIT concludes that a good strategic design and decisions about the locations of data and processing is the key to avoid any disruptive clashes between big data and cloud in future
