When it comes to modern big data systems and related cloud computing platforms, you’d think that storage capacity, processing power and network bandwidth would be the primary elements of an efficient system. It’s becoming increasingly clear, however, that’s not the case, especially as more businesses emphasize data acceleration.
Data acceleration essentially refers to the rate or speed at which large troves of data can be ingested, analyzed, processed, organized and converted to actionable insights. Speeding up these processes, as you might expect, is the acceleration aspect. More importantly, because there is so much that goes into an efficient data system, it’s more of a concept that involves all hardware, software and related tools.
By focusing on data acceleration as a whole, platform developers and network engineers can deliver targeted solutions to improve power, performance and efficiency of these platforms.
By focusing on data acceleration as a whole, platform developers and network engineers can deliver targeted solutions to improve power, performance and efficiency of these platforms. Simply installing a faster server or enabling wider network pathways are just a couple examples of how you can improve a system in the short-term.
However, they don’t offer the real benefits that a truly optimized and efficient platform can. It’s all about operating in the right environment and under the right conditions to create an optimally functioning data facilitation system.
It’s remarkably similar to edge computing — on the surface, anyway. Data is analyzed and handled closer to the source in the edge, to maximize security but also reliability, speed and performance. Data acceleration uses similar principles, except the data in question is not local — it’s still remote. Unique hardware and systems are enabled to mitigate packet loss and latency issues.
Why Does Data Acceleration Matter so Much?
According to BDO, “During the period 2014-2020, the percentage of U.S. small businesses using cloud computing is expected to more than double from 37 percent to nearly 80 percent.” Of course, no one in their right mind would argue against the growth of cloud computing and big data. The point is not necessarily how big or fast that’s happening — just simply that it is.
A growing market means growing demands and requirements. Cloud providers will need to come up with more capable platforms that can store, ingest, process and return the necessary data streams all in real time. Even so, the hardware for doing all this will continue to evolve and get better, bigger and more capable, but that doesn’t necessarily mean it’s going to be efficient.
It’s up to the platform developers and engineers to ensure the appropriate data acceleration limits are not just achieved but maintained. Without acceleration, the community may run into bottlenecks, serious latency problems and even operational failures when the data isn’t processed or returned in time.
Think: trying to deliver a personalized ad to a customer after they’ve left the channel you’re targeting. Or, better yet: an autonomous smart vehicle that is feeding relational data to a remote system and waiting for a response or key insight. Most of the brands and organizations feeding data into the system only have a small window to work with, so efficiency and speed are crucial.
In order for the cloud computing industry to grow to new heights, at least beyond what it is currently, data acceleration will need to become the number-one priority of most — if not all — development teams.
Naturally, you’ll want to research and learn more about data acceleration, if you haven’t already. It’s sure to be a driving force in big data, analytics and machine learning markets for the year ahead.
More brands and organizations will see the value and potential in improving the overall efficiency of their data systems. Those that don’t will need to improve efficiency anyway to meet the rising demands of their networks, customers and channels.