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Alliance of Big Data and DataOps: Opportunities and Possibilities

Alliance of Big Data and DataOps

While Big Data has been around for a while, deep seating its position in the world of analytics along with data repository, DataOps is a different avenue.

Discussions are abuzz with the term DataOps, new DataOps products and solutions are flooding the market by the minute, a higher number of organizations are integrating DataOps with their analytic systems to improve the cycle time, quality, and efficiency of their data analytics. Online educators are chalking up streamlined courses for data sciences.

At this point, DataOps already has a robust ecosystem of vendors who are catering to the rising demand of DataOps in the current market.

Okay, Good. But What is DataOps!?

DataOps bring together multiple cross-functional processes to build, deploy, secure, and monitor data-intensive applications more efficiently. The development team, operations team, security and governance team, data science team, and the data engineering team come together to perform one DataOps process.

It follows an agile development procedure instead of the waterfall model of development. To execute flawlessly, DataOps needs a directed graph-based workflow, consisting all data access, integration, model and visualization step charted out clearly for the data analytics production process.

Beginning its journey as a set of best practices, DataOps today is a new and independent approach to data analytics.

Whatever. Why Join DataOps with Big Data?

DataOps as a concept emerged from the realization that data-intensive applications need a tailor-fit approach from the very scratch. A DataOps implementation is a nod to the fact that data is central to disruptive enterprise applications.

Among the major goals of DataOps is continuous model deployment, and promoting repeatability, agility, productivity, and self-service. It is only obvious that moving data is seen as a performance bottleneck.

Hence,  even though DataOps focuses on processes and people, it requires a single platform, strong enough to provide all the data that the application needs. Instead of having each team work on a different, siloed data platform, it is preferable that a single unified data platform is used to promote collaboration among all organs.

In simpler words, when we are using a DataOps mechanism for an application’s development, management, monitoring; we need a single data storing and management platform so that all the team, irrespective of how diverse and different, can work in collaboration with each other.

That said, what is the first solution that comes to your mind when we talk about a data platform big enough to enterprise-grade reliability, native support to any datatype to accommodate diverse and evolving data sources, multi-tenancy and resource utilization, support for distributed architectures, and self-service data access through a metadata-driven data marketplace; while it empowers the security and governance teams to enforce privacy and security policies with granular access control expression and promoting a self-service, agile data access workflow??

Yours truly, Big Data.

Point Made. But what can this duo do?

Oh, a lot, let’s put it in words for you:

Ensure Agility

With DataOps and Big Data joining powers, new models can be deployed independently of each other, without affecting production applications’ operation or applications under development for that matter.

The focus here is to reduce the ‘to market’ time of the data science and machine learning application with collaboration from various teams, which in itself represents a significant new trend.

Accommodating the different set of considerations that each process has when it is about managing, securing large complex datasets, all the while enabling agile access to that data by people who need it, including the datasets as they emerge, is a paradigm shift.

If this doesn’t ensure agility, only Chuck Norris knows what will.

Increased Productivity

Data scientists do not have to do the ‘plumbing’ for various teams. In nicer words, data scientists do not have to go about finding, curating, copying, and transforming data because Edward from marketing wants to conduct research.

Edward can go to his console, and very well do his work. Also, programmers get a breather as they do not have to refactor the code for the data scientists. Win, Win.

Security

Ever write data access, authentication, and privacy policies? Aren’t those a pain? Imagine having to write those for thirty different teams, on just as many different data stores, and top it off with making it an enterprise size.

Yupp, cumbersome.

But with our dynamic duo, we need just one set of data access, and privacy policies, which can be holistically employed across the whole enterprise. Further, model and applications deployments from anywhere will simply inherit their corresponding permissions.

Did I just hear a cheer from the system administration department? Thought so.

No Performance Bottleneck

As mentioned earlier in the article, moving data is a performance bottleneck in supporting data-intensive applications, and it would be an intelligent practice to consider access, use, and management of data as an organizing principle. And it gets worse than traffic in Mumbai as the data grows in size.

While there is little you can do about Mumbai, combining DataOps with Big Data solutions will come to your aid in data-intensive application production.

So, the gist is,

Even though DataOps and Big Data are two entirely different technologies, when they do come together, they form a workflow structure in which IT is no longer an obstruction or performance issue, but instead is a fluid process in itself which increases productivity by putting emphasis on self-service (remember: no plumbing), and reduces the time to market, while maintaining the enterprise-grade productivity.

Do let us know how you feel about the article in the comments’ section!

(Disclaimer: This is a guest post submitted on Techstory by the mentioned authors.All the contents and images in the article have been provided to Techstory by the authors of the article. Techstory is not responsible or liable for any content in this article.)

Image Source: datafloq.com

About The Author:

Vivek is the President of Consumer Revenue at UpGrad, an online education platform providing industry oriented programs in collaboration with world-class institutes, some of which are MICAIIIT BANGALOREBITS and various industry leaders which include MakeMyTripOlaFlipkart to name a few.

He has 19 years of experience in diversified industries like Consumer goods, Media, Technology Products and Education Services. He has been leading businesses & multi-cultural teams with a consistent record of market-beating performance and building brand leadership. His previous engagement has been with Manipal Global Education services as Sr General Manager, Education Services (Digital Transformation Strategy & Global Expansion).

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