DataOps: understand what it is and the main features

It’s very difficult to talk about growth and strengthening a company without being side by side with technology, isn’t it?

But, amidst so many options and versions within information technology and business intelligence, a fundamental ally for the growth and strengthening of companies, we are lost when it comes to choosing and mastering what is best and most efficient on the market.

Thinking about helping you, we selected the best information about DataOps, which is, without a shadow of a doubt, one of the best solutions within this universe.

Have you, by any chance, heard of her? Next, we will show what it is, how it works and the main developments of the tool capable of uniting, managing and analyzing data with excellence. Follow with us!

What is DataOps?

In a simpler way, DataOps works through the combination of the concepts of DevOps, Agile, in addition to Statistical Process Control, which brings good practices capable of eliminating the complications and barriers that exist between analytical operations and development areas.

It implements some lessons learned from DevOps for data analysis and management. Data operations is not a product, a service, or a solution. They are a methodology, in fact more than that, a culture to improve the use of data through more appropriate collaboration and automation.

DataOps x DevOps: what’s the difference?

We can already verify what DataOps is, and that it drank a lot from the DevOps source, but now the time has come to understand the difference between them, more clearly. From a general perspective, DevOps encompasses software engineering, while DataOps covers the areas of data engineering, data science, analytics and business intelligence (BI).

On the other hand, from a quality perspective, DevOps is focused on code review, permanent testing and monitoring. In the case of DataOps, the processes add a complementary surface to the conventional DevOps steps. This is due to the inclusion of orchestration and testing in the information and data pipelines that DataOps offers.

Thus, there is a division between the locations of development and sources of operation and production areas. Another big difference is that DataOps ensures greater data governance and has greater process control in relation to the processes applied by DevOps.

In short, the two have the same objective, which is to make deliveries without errors, integrate teams, and all of this, in a constant way for customers, but with the difference in the complexity of steps that DataOps has compared to DevOps.

Where does DataOps fit best?

To apply DataOps, it is nice to have the support of professionals with knowledge in data science and also with support from software engineering. After all, with the support of these teams, performance is significantly optimized, and DataOps ends up not being restricted to machine learning.

The concept is tangible for any type of work that uses data and fits perfectly into the microservices architecture.

Functioning in practice

DataOps perfectly interconnects all teams involved in the data cycle, with the aim of using and exploiting it favorably for companies, with agility and at appropriate governance levels.

The culture of data science and data engineering brings all cooperation between infrastructure analysts, developers, support teams, data specialists and other professionals in the field, the work is capable of analyzing and combing fine in each of the steps involving the data and continuously, so it reaches the end user in the most refined way possible.

Aimed at collaboration between developers, infrastructure analysts, support teams and data specialists, the DataOps culture brings together data science and data engineering with the DevOps concept.

So, your main objective is to develop data projects with unparalleled quality, so that the most valuable analytical insights are delivered in a reduced time.

How to implement the system in my company?

Now that you’ve mastered the concept of DataOps and learned about its impact on companies, it’s time to learn how to implement this solution, right? So, follow the tips we have separated to implement the tool below!

Align your IT team

Firstly, to be able to implement DataOps in your enterprise, it is essential to align your IT team, as the tool seeks to integrate professionals and areas.

To do this, hold a strategic meeting of the work to be done, clearly, and allowing the idea to be designed and debated, this way the DataOps functionalities will be able to achieve their maximum level of results for the company.

Test data flows

The second phase of implementation is the data flow testing phase. Here, it is interesting to promote autonomous tests in order to ensure control over the way your data is collected, processed and evaluated.

So, be sure to test data flows, and through this, observe errors and correct failures before finalizing the DataOps implementation.

Use in a variety of work environments

The implementation of the DataOps philosophy must involve the entire company, so it is completely pertinent to put the ideas into practice with the tool in other environments, such as: on extra computers, virtual environments and in containers.
Bet on simple storage

The tip is mainly indicated to facilitate the collection and processing of company data. Since the simpler it is, the more comprehensive it is in the company. To do this, implement cloud computing systems.

Standardize processes

DataOps offers a great possibility of integrating your tools, so it is essential to maintain a certain standardization of your internal activities and do your best not to use different codes and formulas.

Another important point of standardizing your processes is that this will make the team’s work easier when identifying possible failures and errors in the system.

The biggest benefits of DataOps

As we can see so far, implementing a DataOps system can have a huge impact on your company’s projects. And, among so many benefits provided, we have separated the four main universal advantages of DataOps. See and understand each of them:

The system evaluates your process

We know that Data Science has greatly transformed the world of corporations, and more than ever, data management is indispensable for any company with the revolutions of the century. XXI.

And anyone who doesn’t adapt to this will necessarily fail. And, when you adopt the best strategies armed with the best data and information, you become much stronger.

Practices based on DataOps are capable of optimizing all processes involving data, in this way, DataOps guarantees the best possible delivery of data, and what’s more, it also provides analysis of your processes.

Improves collaboration

DataOps directly relies on interdisciplinary squads. The squads play their role as an interdisciplinary team on site, or even in a nearshore/offshore scheme, depending on what suits you best.

In short, you can have several professionals from different areas operating simultaneously in different phases of your project. And all this exchange is provided by DataOps, the same can be done at any stage with your own clients. Ultimately, collaboration is key in DataOps.

Reduces error rates

With DataOps, the number of failures and errors decreases, this is because the management system is much more automated, with versioning and governance of everything related to data.

This way, existing errors are identified more easily and adjustments can be made much more accurately and quickly, significantly increasing accuracy when delivering data.

DataOps shortens the change cycle period

Now, we know that the long cycle time is one of the biggest challenges when it comes to decisions made based on the data provided.

The phenomenon occurs because the most archaic processes are too anachronistic for today, and keeping up with advances in technology is essential for any company.

DataOps is the best solution for this, as it significantly speeds up the change cycle time, due to the constant tests that evaluate and monitor in a much more efficient and faster way.

In the digital age, data excellence is essential to remain competitive and efficient, so it is always important to be able to implement the best innovations.

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