Standardization prepares your data for the long haul
We all know the futility of reinventing the wheel, but if your wheels aren’t aligned—that is, if they aren’t the same size or aren’t suitable for their track—you need to reassess how you’ll be getting things where they need to go. Whether for two-wheeled donkey carts, Victorian steam engines, the Model T or that Segway your workplace has been eyeing, standardization holds the key to using technology to efficiently move things from one place to another.
Moreover, standardization can speed us toward data goals, much like how entrepreneurs experimented with wheel and track configurations in the early railroad days, the better to move vast quantities of people and goods across long distances. Or take long-haul trucking, in which rigs use standardized tires and containers to transport items from ports to points inland. And the incentive for standardization is great: If you arrive first, entire markets can become yours.
Driving innovation through data
In much the same way, moving large volumes of data across a landscape of multiple systems requires standardization on open architecture to take advantage of data no matter where it resides. But reality is often quite different. Huge amounts of information lie dormant in siloed systems—or are discarded because the wheels that move information can’t carry the weight of data or traverse mixed configuration and hardware environments.
That’s where Hadoop comes in. Hadoop was created to cost-effectively store data regardless of its source, combining commodity hardware with a self-healing file system. The open-source software framework is designed to scale from a single server to thousands of machines while offering a high degree of fault tolerance. Hadoop is meant to process data no matter where it hits the road or what track it’s placed on.
But smoothly adding capabilities from multiple sources can be a different matter entirely—which is why the Open Data Platform (ODP) Core aims to become the standard for building big data solutions that incorporate a solid foundation of standard Apache components. Instead of trying to fit together items never designed to work in conjunction with each other, ODP Core helps ensure that capabilities interlock, allowing you to quickly and confidently lay down a Hadoop infrastructure and start collecting data to expand your view of your business and your customers.
ODP Core also helps you keep your data rolling for the long haul. Regardless of size or location, the powerful technology governed by the framework makes data collection easy. Moreover, this framework avoids obsolescence, as well as the complex management roadblocks found in proprietary systems.
Opening up the opportunity throttle
The ODP framework aims to provide a way of harmonizing big data deployments, accelerating Hadoop into data environments and allowing customers to build out big data applications that can reduce the need for constant verification and versioning. What’s more, the ODP can aid more than just Hadoop distribution vendors. Enterprises and ecosystem vendors alike can benefit by significant simplification that allows them to verify versions and distributions of Hadoop applications once, then run them anywhere across their preferred big data infrastructure deployments. Increased choice results, bringing ever more big data applications and solutions.
IBM is among the more than 30 members of the Hadoop ODP, all of whom stay current with Hadoop ODP distributions and upgrades. In doing so, they create interoperability that enhances performance throughout the Apache Hadoop ecosystem, promoting easy adoption of big data solutions and allowing powerful analytical tools to be readily developed and integrated that can deliver deep insights into data—regardless of its size, origin or ultimate destination.
To find out how your organization can move data without reinventing the wheel, start your Open Data Platform journey today.