Big Data
| Traditional approaches to data integration that rely on moving data, are struggling to handle the extreme volume and diversity of Big Data. Data virtualization has emerged to address the need for real-time, universal access to data, regardless of format or location. |
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No person would ever discount the importance that data plays in today’s business environment. This data has grown exponentially over the years—whether it’s in the form of streaming data, legacy data, or even operational data—and it is moving faster than ever before. All told, this data is completely transforming the face of business, and it is no incumbent upon businesses to have a comprehensive solution for dealing with it in order for those businesses to ensure their long-term success.
Dealing with all of this data presents a number of technical challenges. But, it also presents a number of tremendous opportunities. Further, not all of the data that businesses have to contend with these days is transactional. Some of it is machine-to-machine based data, as is the case with RFID tags, and some of it has to do with regulatory compliance, as is the case with data in the financial sector.
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Having ready access to this data is incredibly important for every business’ analytics and business intelligence. However, facilitating such ready access requires a great deal, namely the realignment of data closer to analytics. Also, such ready access requires that non-relational data and relational data be seamlessly blended together. To accomplish this, traditional methods of accessing this data, which require data to be physically moved, must be done away with.
No one can doubt the importance of offering this level of convenient access to data. Both decision makers and customers have been conditioned by our modern society to expect access to data on the fly. However, just because this expectation has been conditioned doesn’t mean that meeting that expectation is easy. There are a number of obstacles that must be overcome in order to facilitate this. Most importantly, a solution must allow for data from multiple sources to be integrated together and standardized, and the data must be made consistent across the business side of things and the customer side of things.
The way to accomplish this is through a solution that virtually combines data indiscriminately of where that data comes from. If this can be accomplished, then the BI tools and analytics tools that a business employs can be used to their maximum effectiveness. However, the majority of businesses do not deploy such a solution. Rather, they rely upon the outdated ETL method, which stands for Extract, Transform and Load. While this method may have been sufficient in the past, it isn’t sufficient any longer. Such a method fails to meet the need of timely access to data, as the data must be physically moved for the process to work. Further, the process of physically moving the data reduces the consistency of the data, introducing a number of additional costs and complexities for businesses.
Mainframe data virtualization is the answer, as it places data next to the analytics software used to analyze that data. It accomplishes this by diverting the process of data integration to specialized processors – IBM System Z processors – that operate in tandem with a mainframe’s central processors. There are no software license charges that need to be considered with this method, and MIPs capacity is not affected by the process of data integration. Because of this, the production of data on the mainframe is undisturbed and TCO is dramatically reduced.
The problems with latency, consistency, and accurateness experienced with ETL methods are not experienced with mainframe data virtualization. In fact, those problems are entirely eliminated. Such a method allows data to be easily accessed through and dealt with through BI tools and analytics tools, and the problem of dealing with unfamiliar mainframe environments is eliminated.
All told, this empowers the decision makers of businesses to meet their goals of mitigating risk and driving expansion. Timely and accurate data is put right in their hands, empowering these decision makers to successfully meet the demands of their customers, identify threats in the market place, and even to attack new business opportunities. Mainframe data virtualization is the future, and it leaves everything else in the dust.
| By Mike Miranda | Embed |
Author Bio - Mike Miranda is a writer and PR person for Rocket Software.
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