Big data storage opens new perspectives for both large and small companies to identify new opportunities for improving customer service, improving the efficiency of sundry processes, identifying formerly unnoticed sample templates, etc.
However, so as to take advantage of the large data, in addition to analytical software and data integration software, an appropriate infrastructure is required for storing, processing and transferring data.
In view of the huge volumes of big data, the infrastructure for their storage and processing becomes particularly important. Few companies and organizations have the necessary capacity for this. But the problem is not so much a lack of room as in the inadequacy of traditional storage for large data problems. The issues of choosing and building the necessary infrastructure have been the subject of discussion.
Big Data Storage Solutions and Goals of Businesses Nowadays
The platform for storing big data must solve two opposite tasks. The big data storage solutions that tackle the classical storage, are somewhere in between the online data and long-term storage data. Valuable information can be contained both in data that comes in real time – which must be instantly responded to, and in data that is gathered over the years and which contains a rich history. Modern storage architectures effectively cope with both loads thanks to new big data services.
Now the idea of "data lakes" has been recognized, where it is possible to record data from a variety of sources in its original form and efficiently process it. The architecture of the data lake has evolved significantly. Now it assumes the presence of another level of processing - a memory level, or Speed Layer. It adds new processing capabilities to the real-time stream. Queues, streaming downloads, etc. have been added to distribute big data flows between different levels of data storage and processing.
Do not forget to read about data virtualization.