Incorporating ML For Data Analytics Can Fathom Big Data Storage Concerns
Machine learning algorithms are discovering hidden insights into the hidden business value through existing data across multiple industries. But the fact cannot be ignored that ML for data analytics imbibes some threat and challenges when it comes to storage infrastructure.
As the data can contain the hidden value, the organization may be less concentrating on the removal of old and aging data which causes storage issue. This may also result in complicating capacity planning efforts. Not to forget that the actual analytical processes create an extra load on existing storage infrastructure.
In contrast to this, several vendors have started using artificial intelligence as a tool for solving problems generated by big data analytics. Presently, these vendors have not based their ML for analytics effort around one technology rather on a distinct bunch of technologies.
While considering AI for workload profiling and capacity planning, organizations much focus on having access to current data about storage use and health. Also, depending upon the real-time data is not always desired.
The disadvantage of relying on real-time streaming data is that it is raw and uncurated possessing possible imperfections and it limits the amount of processing that can be done.
To curb this, using relatively current data (not real-time) can process more information through ML for analytics.
Additionally, once can also opt for Lambda Architecture which addresses this problem by streaming data in two varied layers. The layers are known as a batch layer and speed layer. The former’s job is to store data as it is not being acted on in real-time. The batch rule can also be employed to improve and enhance data quality. Additionally, in certain models, it can also make data available to the serving layer, which is the third layer. This additional layer creates batch views in response to query requests.
While in the speed layer, inbound data is streamed to provide real-time data views. For Lambda architecture to work efficiently, it should possess low latency and have enough scalability to accommodate the inbound data.
Although the Custom FPGAs have been used in electrical engineering for a long time yet is relatively a novel idea to the IT industry. As it stands now, hardware vendors have started to use it as an alternative to CPUs and GPUs in ML for data analytics offerings. For a fact, Intel spent $16.
