DEALING WITH THE NUANCES OF BIG DATA

Dealing with different types of data is converging, thanks to utilities and applications that can process the data sets using standard XML formats and industry-specific XML data standards (e.g., ACORD in insurance, HL7 in health care). These XML technologies are expanding the types of data that can be handled by Big Data analytics and integration tools, yet the transformation capabilities of these processes are still being strained by the complexity and volume of the data, leading to a mismatch between the existing transformation capabilities and the emerging needs. This is opening the door for a new type of universal data transformation product that will allow transformations to be defined for all classes of data (structured, semistructured, and unstructured), without writing code, and able to be deployed to any software application or platform architecture.

Both the definition of Big Data and the execution of the related analytics are still in a state of flux; the tools, technologies, and procedures continue to evolve. Yet this situation does not mean that those who seek value from large data sets should wait. Big Data is far too important to business processes to take a wait-and-see approach.

The real trick with Big Data is to find the best way to deal with the varied data sources and still meet the objectives of the analytical process. This takes a savvy approach that integrates hardware, software, and procedures into a manageable process that delivers results within an acceptable time frame—and it all starts with the data.

Storage is the critical element for Big Data. The data have to be stored somewhere, readily accessible and protected. This has proved to be an expensive challenge for many organizations, since network-based storage, such as SANS and NAS, can be very expensive to purchase and manage.

Storage has evolved to become one of the more pedestrian elements in the typical data center—after all, storage technologies have matured and have started to approach commodity status. Nevertheless, today’s enterprises are faced with evolving needs that can put the strain on storage technologies. A case in point is the push for Big Data analytics, a concept that brings BI capabilities to large data sets.

The Big Data analytics process demands capabilities that are usually beyond the typical storage paradigms. Traditional storage technologies, such as SANS, NAS, and others, cannot natively deal with the terabytes and petabytes of unstructured information presented by Big Data. Success with Big Data analytics demands something more: a new way to deal with large volumes of data, a new storage platform ideology.

Taken from : Big Data Analytics: Turning Big Data into Big Money