NIST Releases Final Version of Big Data Interoperability Framework
Just about every industry can benefit from the collective power big data offers. The amount of time and manpower it takes to utilize the information contained within this data often deters organizations from attempting to take advantage of this resource. The National Institute of Standards and Technology has been working to improve its framework to eliminate many of those stumbling blocks. NIST finally released its final version of Big Data Interoperability Framework.
At the end of October, NIST released the final version of its Big Data Interoperability Framework (NBDIF), designed to assist in all sectors in ways to deploy software tools that analyze data and employ secure data sharing without retooling the computer environment.
The framework fills a long-standing need among data scientists, who are asked to extract meaning from ever-larger and more varied datasets while navigating a shifting technology ecosystem. Interoperability is increasingly important as these huge amounts of data pour in from a growing number of platforms, ranging from telescopes and physics experiments to the countless tiny sensors and devices we have linked into the Internet of Things.
For example, several years ago the world was generating 2.5 exabytes (2.5 billion gigabytes) of data each day, and that number is predicted to reach 463 exabytes daily by 2025. (This is more than would fit on 212 million DVDs.)
Computer specialists use the term “big data analytics” to refer to the systematic approaches that draw insights from these ultra-large datasets. With the rapid growth of tool availability, data scientists now have the option of scaling up their work from a single, small desktop computing setup to a large, distributed cloud-based environment with many processor nodes. But often, this shift places enormous demands on the analyst.
This framework is an effort to help address these problems. As with the draft versions of the framework NIST has released previously, the final includes consensus definitions and taxonomies to help ensure developers are on the same page when they discuss plans for new tools. It also includes key requirements for data security and privacy protections that these tools should have. What is new in the final version is a reference architecture interface specification that will guide these tools’ actual deployment.
When applied to a big-data-related problem, the value of the framework will be in helping analysts speak to one another and more easily apply all the data tools they need to achieve their goals.
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