The term “Big Data” is a buzzword in the world of technology. Big Data projects lead the day. However, there always are ways to increase their efficiency further. One of them is using Big Data and DevOps together. Grab some insights from our DevOps services team at WishDesk about why Big Data needs DevOps.
What is Big Data?
Big data means large and complex data sets from a variety of sources. Their volume and complexity are so big that traditional data processing software cannot manage them. On the other hand, such data are able to resolve business tasks that traditional data cannot.
Dealing with Big Data involves processes like obtaining the data and then storing, sharing, analyzing, digesting, visualizing, transforming, and testing, to provide the expected business value. Companies experience a huge pressure on the competitive markets for a faster delivery of their challenging project. How to provide all this with maximum efficiency? Here is where DevOps comes in, bringing the right tools and practices.
What is DevOps?
DevOps is a methodology, culture, and set of practices that aims to facilitate and improve the communication and collaboration between development and operations teams. It is focused on automating and streamlining processes within the project’s development lifecycle.
Important pillars of DevOps are shorter development cycles, increased deployment frequency, rapid releases, parallel work of different experts, and regular customer feedback.
The speed, reliability, and quality of the software delivery significantly increases with DevOps. These are just some of the reasons why DevOps is important for software projects.
DevOps and CI/CD: how are they related?
In all discussions of DevOps, you will hear the terms CI or “continuous integration” and CD or “continuous delivery” of the software. They are inherent to the DevOps practice:
Continuous Integration (CI) is the practice of merging the code changes from multiple developers into the central repository several times a day.
Continuous Delivery (CD) is the practice of software code being created, tested, and deployed to the production environment on a constant basis.
Why Big Data needs DevOps
As mentioned above, Big Data projects can be challenging in terms of:
handling large amounts of data
delivering the project faster to keep up with the competitive market or due to the pressure from the stakeholders
responding to changes faster
The traditional approach, as opposed to DevOps, is not good at resolving this. Traditionally, different teams and team members work in isolation. For example, data architects, analysts, admins, and many other experts work on their part of the job, which slows down the delivery.
On the contrary, DevOps, according to the above described principles, brings together all participants of all stages of the software delivery pipeline. It removes barriers, reduces silos between different roles, and makes your Big Data team cross-functional. In addition to a huge increase in operational efficiency, this gives a better shared vision of the project’s goal.
With all this, it’s no wonder that embracing DevOps and including data specialists within the CI/CD process is becoming a standard practice among Big Data companies. Let’s outline a few things that they gain:
Minimum error risks
The challenging character of Big Data increases the chance of errors in the software creation and testing. DevOps will help you minimize them. Thanks to continuous testing that starts at the earliest stages, errors can be spotted in time or prevented completely. Your project has a high likelihood of reaching the production stage flawlessly.
Software works as expected
When data specialists are closely involved in collaboration with other specialists, they help them understand the specifics of data that software will deal with in the real world. As a result, the real-world behavior of the software closely matches its behavior in the development and testing environments. Considering the complexity and diversity of real-world data, this is very important.
Better planned software updates
Similarly, if developers collaborate with data experts before writing the code and gain an in-depth understanding of all types of data sources the Big Data application should work with, they can plan future software updates more effectively.
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