This simple concept helps explain the rise of cloud and edge computing within the era of big data.
Data gravity is a term coined by GE Engineer Dave McCrory in a 2010 blog post, referring to the way data “attracts” other data and services. Just as a planet’s gravity pulls on other objects, this analogy imagines that large accumulations of data are more likely to accrue even more data.
It’s a useful analogy in that it explains why both cloud computing and edge computing have come to dominate the way that data is stored and managed. So far, this theory has generally held true for enterprises seeking to manage their expanding stores of data.
Some 2.5 quintillion bytes of data are generated each day, so the question of how and where to most efficiently store this data will only become more urgent. Moving huge volumes of data can be a major challenge, and often demands creative solutions. Services like Amazon Web Services’ “snowmobile,” a storage data center delivered by a semi truck, constitutes one of the more extreme measures. With this on-site option, enterprises are able to back up in a few weeks what would otherwise require years to send to the AWS cloud.
Effective data analytics and artificial intelligence rely on these increasingly massive data streams, but in order to use all this data with reasonable efficiency and speed, applications have to be close to the source. All things being equal, the shorter the distance, the higher the throughput and the lower the latency. These limitations form the basic principle behind data gravity.
Many businesses use data to enable their operations, from internal data like financial files, to external data like weather information or statistics around customer behavior. For these enterprises, data gravity imposes certain limitations on how they do business.
Traditionally, data was generated and stored internally and on-premises. This made it easy for analytics platforms to “own” the data they were crunching. But as the amount of external data increases, applications have to migrate data from other platforms in order to analyze it. These days, businesses tend to store and access their data using hybrid solutions that include both cloud, and on-premises storage. But businesses don’t just want to store data — they want to use it.
For over a decade, data storage has been migrating to the cloud. The percentage of enterprises processing data in the cloud grew from 58% in 2017 to 73% in 2018, reflecting improved tools and scalability — and certainly contributing to further data gravity within the cloud.
But the IoT presents a new set of challenges in dealing with data, as it exists at the periphery of the network, meaning the data it produces may not be easily utilized by centralized applications in the cloud. This is especially true considering the deluge of data being produced by these increasingly critical devices. At the same time, for IoT devices in factories, hospitals, or automobiles, low latency data processing is an absolute necessity. Edge computing seeks to solve this problem by bringing data processing right to the device, avoiding the bandwidth costs and slowdowns associated with pushing data to the cloud.
Across the board, IT teams and networking solutions providers are working to enable enterprises to better use this data. IBM recently overhauled its Cloud Private for Data platform to allow customers to use analytics wherever their data is, whether that’s in an IoT device or a private cloud. And Microsoft’s “Intelligent Edge” strategy calls for enabling software to run on IoT devices, using ever-smaller processing hardware.
Enterprises are eager to generate and make good use of the data to which they have access — and for good reason. Data analytics is increasingly providing the insights they need to remain competitive. Many industries have already come to rely on the operational advantages provided by IoT devices. But just as many IT departments are struggling to effectively manage the storage and processing of all that data.
If your business is looking to overcome the limitations of data gravity, it may be time to pair with the experts at Turn-key Technologies (TTI). With decades of experience and a thorough understanding of data storage and management, TTI has the expertise necessary to help modern-day enterprises implement cloud and edge computing solutions that will help them capitalize on their data with the speed and cost-efficiency the modern marketplace demands.
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