DevOps and big data are not similar or even related terms. Regardless of the fact that at first they seem to have no connection, the combination of these two terms forms a strong synergy that can improve your business.
Big data refers to large and complex data sets that require specialized tools and techniques for analysis. This is done by specialists such as data scientists and engineers.
Effective management of big data implies skillful management of simpler processes such as:
In order to be useful for business, data must be clearly defined and interpreted.
DevOps is the combination of organizational structure, tools, and practices that enable you to distribute your applications and services faster and simpler. An effective DevOps strategy brings very good results.
Some of the characteristics that are associated with DevOps are:
Software is no longer an optional component of a successful business. It's necessary for various segments such as logistics and communication.
This is precisely why the importance of DevOps is increasingly prominent in modern society.
Regardless of the involvement of experts (data scientists and engineers) in the management of big data, the process itself is still extremely demanding.
With DevOps, you can achieve a unified vision of individual parts of the business.
If teams work in isolation, there are obstacles in communication and a lack of clarity. DevOps helps data analysts, scientists, and engineers become cross-functional and therefore improving the efficiency of individual tasks and saving time.
One of the most important characteristics of DevOps is that the development tries to mimic the production environment. With big data, this is very important because you reduce the risk of (big) mistakes.
Given that the communication barriers between the teams have been lowered, data specialists can express more clearly the importance of particular pieces of data to other teams. They can also explain to developers what data the software will encounter in production. This minimalizes errors and provides additional context for efficient work.
With this in mind, collaboration with data experts helps plan effective software updates.
Working with big data is challenging. Complex software implies a greater possibility for errors. But if the mistakes are quickly spotted, you can save a lot of time and effort that would otherwise be focused on fixing them.
With clear communication and cooperation, potential errors can also be predicted. This means that certain protocols can be planned in advance.
This would not be possible with the traditional approach, but with DevOps, it is very simple.
The software release is only one of the steps in the development chain. After it is released into production, it is necessary to monitor its performance in the real world.
There are many questions regarding software that need to be answered. Some of them are the following:
One of the simpler ways to get these answers is to collect feedback. After that, it is important to interpret it with data experts and make a purposeful plan for the upcoming updates.
Applying DevOps practices in a big data environment offers many benefits. The transition from a traditional (waterfall) to a DevOps organizational structure allows teams to understand each other better and produce better results. With big data, this is especially important.
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