Big Data Management: DevOps Best Practices

  • By Miley Downing
  • 01-12-2018
  • Big Data
big data management
You’ve Got The Tools, Use Them
Big Data refers to statistical analyses of larger and larger data sets as made possible through cloud computing and the internet of things. Internal DevOps efforts, apps, and more can be monitored via cloud through Big Data. It’s not just the size of microprocessors that initiates the sort of exponential Moore’s Law development which has contributed to Big Data’s rise.
As a point of reference, in the sixties, Gordon Moore noticed that computational ability doubled at approximately 24-month intervals. For varying reasons today, that interval is reckoned at about eighteen months.
Technology innovators have gotten silicon down to the NM, or nanometer, range now; and it’s predicted that circuitry on the level of one nanometer is theoretically possible in the near future. That’s basically an atomic distance, it’s so small. It’s essentially the distance between two silicon atoms. For reference, a strand of DNA is 2.5 NMs in diameter.
That level of circuitry as yet has not arrived, but it’s on the near horizon. However, it’s important to note that internal circuitry isn’t the only region where computational development expands computational ability. With cloud computing, there is additionally exponential expansion.
big data
What Makes Big Data So Effecting?
If one computer could process a gigabyte in an hour, two networked together could do it in a half hour, four in fifteen minutes, eight in seven and a half minutes, sixteen in three minutes and three fourths of a minute, etcetera. There are servers networked together in the millions today for big companies like Amazon, making it possible to process terabytes instantaneously—and this essentially represents the engine of “Big Data”.
Cloud computing in conjunction with IoT (Internet of Things) tech is increasing the speed at which massive data sets can be processed in an effective way allowing the results to be translated and acted upon. When it comes to DevOps, utilizing Big Data can reveal trends which help you.
For example, if you had a supply chain fleet organized across a national or international geographical area, you could use IoT installed in the vehicles to give you a real-time picture of transit, and help you manage things such that you save tens of thousands of dollars every day through more effective transit solutions.
You can monitor weather, traffic patterns, road construction, and trends over time to help reveal whether actual distance reflects travel time. Sometimes it’s faster to go “the long way” because there’s less traffic. While more fuel and wear-and-tear is absorbed, time and money are saved through more efficient product delivery, overcoming the extra distance.
New Possibilities Previously Unavailable
Determining such things is hardly possible without Big Data. Now let’s look internally at your operations to see how DevOps specifically can benefit. Essentially, with any new operational plan, or optimization strategy, you’re going to need to make projections concerning cost. You’ll need to develop a budget from those projections using known effective strategic solutions, and it’ll be best if that budget is “cushioned” against the direct cost of projection.
With Big Data, you can more accurately project expenses based on real numbers collected in real time continuously. You can gauge prospective shifts in operations against previous ones of a similar nature, comparing and contrasting them such that you’re able to more accurately project how a new project is going to go.
In order to help you organize and attain metrics, you’ll need software solutions specifically designed for this purpose; you can find out more here. Essentially, as Big Data becomes more integral to businesses large and small, there are options specifically developed to help operations capitalize from this information.
Because Big Data is primarily facilitated through cloud computing and IoT, even a small startup stands to capitalize from Big Data utility—they’ve just got to be creative about it. Algorithms and monitoring solutions can be designed which keep a digital finger on the pulse of the market as concerns peers and competitors.
big data cloud
Big Data Implications Going Forward
What this ultimately does is give you the ability to identify operational trends which could affect your DevOps strategies. It’s important to realize that DevOps aren’t only influenced by internal operations. If your business starts to struggle, so will DevOps. Ideally anything you design going forward should be optimized for greatest prospective success.
Also, you’ll want to monitor varying projects as they’re applied to existing operations. Big Data can help you monitor varying operational shifts initiated internally to help you gain a more clear, actionable picture of infrastructure. This helps you determine where you need to alter initial strategies, and where you need to keep successful plans running along smoothly.
There are quite a few different ways cloud computing, Big Data, IoT, and DevOps intersect. What’s important to takeaway here is that visibility of technological solutions is greater than perhaps it’s ever been, owing in large part to new shifts which come from these trends. Not using that visibility to inform forward momentum is essentially leaving money on the table.
Especially if you’re a smaller operation, you can’t afford to do this. If you’re a larger operation, you can comprehensively optimize in ways that save you millions every year. The takeaway? Apply a clearer DevOps strategy through information utilization strategies employing Big Data.

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Miley Downing

Miley Downing is the editor with who helps digital businesses reach their full online potential. She is passionate about programming and IT consulting. Her current focus is helping SaaS businesses create better world for our kids. She frequently writes about the latest advancements in the digital and tech industry.