Big Data and DevOps: Why They Are Better Together on A Project

“Big Data” is a term you will often hear in the world of technology. Big Data projects are interesting and challenging. One of the ways to increase their efficiency is to use Big Data in combination with DevOps. Here is a little tour of Big Data and DevOps: what they are and why Big Data needs DevOps.

What is Big Data?

Big data is known as large sets of data from multiple sources. Because of their volume and complexity, they are hard to manage by traditional data processing software. The other side of the medal is that Big Data can resolve complex business tasks that traditional data cannot.

Specialists who work with Big Data need to find effective ways to obtain, store, share, analyze, visualize, transform, test the data, and so on. The competitive market dictates its requirements as to the fastest delivery of the software product. This is where DevOps, with its tools and practices, is of great help.

What is DevOps?

DevOps is defined as a methodology, as well as culture and set of practices, that are aimed at facilitating and improving the collaboration between the development and operations teams. This is reflected in the term “DevOps” that unites the development and operations. 

DevOps increases the speed, reliability, and quality of software delivery. It automates and streamlines the processes of a software project’s lifecycle. The key principles of DevOps include shorter development cycles, more frequent deployments, faster releases, parallel work of different experts, and constant feedback from the customer. 

A Glimpse at DevOps and CI/CD

The terms CI “continuous integration” and CD “continuous delivery” are also frequently met in all DevOps discussions. They are important DevOps practices:

• Continuous Integration (CI) is the practice of regularly merging, or uniting, the code changes from multiple developers into the central repository. 

• Continuous Delivery (CD) is the practice of creating, testing, and deploying the software code to the production environment on a constant basis.

Why Big Data Needs DevOps

While defining Big Data above, we mentioned that projects of this kind can be challenging in terms of:

• Handling large amounts of complex data 

• Delivering the project faster in order to stay competitive on the market

• Responding to changes very quickly

Without DevOps practices, it’s hard to resolve. Traditionally, different teams and team members (data architects, analysts, admins, etc.) work in isolation on their part of the job. This is not favorable for quick delivery.

Contrary to this approach, DevOps brings together all participants of the software delivery pipeline and removes barriers between them. Your Big Data team becomes cross-functional, increases its operational efficiency, and gets a better-shared vision of the project’s goal. 

This makes it clear why Big Data companies more and more often rely on DevOps practices and involve data specialists in the CI/CD processes. Here are just a few benefits of doing this:

Error Risks Are Minimal

Dealing with large and complex data sets may lead to human eros in software development and testing. DevOps minimizes these risks thanks to continuous testing from the earliest stages. Errors are found in time or prevented totally.

The Software Works According to The Expectations

Data specialists, when closely involved in collaboration with other experts, help them understand the specifics of real-world data that software is going to deal with. Thanks to this, when the software is released, its work closely matches the way it worked in the development and testing environments. This is vital because Big Data in the real world can be very complex and diverse.

Software Updates Are Better Planned

Similarly, the collaboration with data experts before writing the code gives developers an in-depth understanding of all types of data sources their application will meet, so they can plan the future software updates with maximum efficiency.

Data-Related Processes Are Streamlined

Combining DevOps and Big Data helps streamline time-consuming processes such as data migration or translation, as well as improve the data quality. In addition, with the most tedious processes streamlined, your experts are free to focus on creative work.

Continuous Analytics Is Provided

Your project will benefit from another useful DevOps practice such as continuous analytics, which streamlines the processes of analyzing the data and automates them via algorithms. 

Feedback Is Full and Accurate

After deploying the Big Data project to production, experts need to gather real-time feedback on its work. The close collaboration between the admins and data scientists helps get the most accurate feedback.

This has been a quick summary of what makes DevOps important for Big Data. See more stories about DevOps, as well as other IT topics, from the WishDesk web agency.