Understand the main differences between DevOps x DataOps

DevOps emerged to revolutionize the area of ​​software development . DataOps, on the other hand, emerged to transform the area of ​​data science and analytics .

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Read until the end and discover what DevOps and DataOps are, what is the difference between them and, based on these definitions, discover how to improve the quality and agility of deliveries in your company. Let's go!

What is DevOps?

DevOps (software development and software operations) refers to a new software development culture that encompasses the set of practices developed to improve integration and collaboration between development and operations teams in companies.

This encourages the union of these teams and, thus, they can collaborate to develop, test, deploy and also monitor software and applications with greater quality and control.

Why has DevOps become a necessity in modern business?

Traditionally, the areas of development and operations are different sectors, which have different motivations and responsibilities . Therefore, communication between these two areas often ends up being a major challenge for business.

And why does this happen?

In practice, the daily routine of these team areas works like this:

Developers are responsible for delivering value in the form of functionality in applications, while operations teams are responsible for maintaining the stability of these applications.

In other words, in traditional management, it is very common for there to be a collision between these teams. As a result, they end up moving in opposite directions, and, as a consequence, harm the progress , quality and delivery of projects . And DevOps emerged precisely to solve this!

Based on the principle of collaboration between the development and operations teams, DevOps assumes that all software projects must be managed jointly by these two teams, ensuring that deliveries are continuous, fast and efficient.

But why is DevOps considered a culture?

DevOps is considered a new project management culture because it is based on the following pillars:

1. Continuous integration: code changes made in a central repository, so that creations and tests are automated and executed.
2. Continuous delivery: automated and frequent deployment of new versions.
3. Continuous feedback: frequent feedback at all stages.

In other words, it is a culture that adds more value to business and increases companies' ability to respond to changes through fast, high-quality deliveries by allowing teams to focus on creating code, eliminating overload and human errors.

What is DataOps?

Never heard of DataOps? Follow us, we will explain everything about this revolution to you!

Inspired by the DevOps revolution, DataOps was born to accelerate data intelligence in companies, through collaboration and process optimization in data projects.

How does this work in practice?

DataOps seamlessly connects teams involved across the entire data cycle, with the aim of using and exploring the value of companies' data, quickly and at appropriate governance levels.

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.

We explain!

DataOps inserts a company's data cycle into a virtual space and determines structured workflows between data, development and operations teams, facilitating communication, collaboration and data analysis at all stages of the chain, so continuous and reliable, all the way to the end user.

Therefore, the main objective of DataOps is to develop quality data projects that satisfy the needs of companies, delivering valuable analytical insights in reduced time.

DevOps x DataOps: what's the difference?

Now that you know DevOps and DataOps, let's understand what the difference is between them?

From a general perspective, DevOps encompasses software engineering, while DataOps navigates the areas of data engineering , analytics, data science and business intelligence (BI).

In turn, from a quality point of view, DevOps focuses on code review, continuous testing and monitoring. DataOps processes, on the other hand, add a complementary layer to traditional DevOps steps.

This is because DataOps includes the orchestration and application of tests on data pipelines. As a result, there is a separation between the development and data environments of the operations and production areas. Furthermore, DataOps is also more concerned with data governance and process control in relation to DevOps.

In short, the two areas have the same objective, which is to integrate teams and deliver error-free and continuous deliveries to customers. The big difference is that DataOps will add more steps, focusing on data processes.

Understand the differences below:

• DevOps focuses on the development, integration and continuous delivery of software.

                                                          DevOps process phases:

                Development > Construction > Testing > Delivery > Administration

• DataOps focuses on creating and developing robust data products .

                                                         Phases of the DataOps process:

                                        Analysis > Development > Orchestration

                                 > Testing > Delivery > Orchestration > Administration

A culture of DataOps na Information

Just as DevOps is revolutionizing modern software development, DataOps is transforming the processes for creating efficient data products and designs.

At Indicium, we are always updating ourselves and using the best and most advanced technologies on the market. Therefore, our teams are agile and focused on the DataOps culture .

The result? Quality, efficient projects that deliver value in the daily lives of our customers.

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