The Differences Between DataOps and DevOps in Software Development

DataOps is a software development trend that has been growing more popular over the last few years. The trend combines software developers with data scientists to create software products for customers. On the other hand, DevOps is an IT management philosophy that focuses on communication and collaboration between software developers and other professionals in order to streamline software delivery. The focus of this article will be discussing some of the differences between DataOps and DevOps in software development.

What are The Key Differences Between DataOps and DevOps?

The key differences between DataOps and DevOps are their job descriptions, how they communicate with each other, and their software development goals. A data scientist is responsible for working with software developers to create software products that use data. Software developers are responsible for creating new features, as well as fixing bugs within software applications. 

Both DataOps and DevOps must work together in order to develop software products for customers successfully. The two groups must communicate with each other regularly so they can understand what one another is doing in software development.

DataOps is a software development trend that has been growing popular over the last few years. Besides, DataOps combines software developers with data scientists to create software products for customers. On the other hand, DevOps is an IT management philosophy that focuses on communication and collaboration between software developers and operations professionals to streamline software delivery. Below we discuss some of the major differences between DataOps and DevOps in software development.

The human factor: software as a business asset

In DevOps, the software is viewed as an important part of an organization’s assets. Within DataOps, the software is treated more like raw data, and it will be analyzed to extract information or used in different ways that can help create better software products for users.

The goal of DevOps when involving software management is to shorten the feedback loop and increase software quality. On the other hand, the goal of DataOps is to extract valuable insights from various data sets in order to create software products that are more useful for users than existing software applications.

DevOps is focused on onboarding software as quickly as possible, with a focus on quality being a priority. Contrarily, DataOps work involves using data scientists that will help determine how software should be developed based on data sets or software products that already exist.

The focus of DataOps is to create software, and software development teams are secondary, while the primary focus in DevOps is software developers, and operations professionals are secondary.

There’s an expectation of using automation tools for software development and software deployment in a DevOps environment. At the same time, DataOps is focused on using data science to extract valuable insights from the data.

The process: software development

DevOps software developers are secondary, while the primary focus is software. DataOps works to onboard software as quickly as possible for users, with quality being a priority, while DataOps work involves using data scientists that will help determine how software should be developed based on data sets or software products that already exist.

The goal of DevOps is to shorten the feedback loop and increase software quality. The goal in DataOps is to use data scientists to extract valuable insights from various sources such as databases, sensors, or software applications to create better software products for users. DataOps aims to extract valuable insights from various data sets to create software products that are more useful for users than existing software applications.

DevOps is focused on onboarding software as quickly as possible, with a focus on quality being a priority. At the same time, DataOps work involves using data scientists that will help determine how software should be developed based on data sets or software products that already exist.

The process of DevOps can vary depending on who is involved and the team working together as software developers, operations professionals, and software development managers are involved. The focus of DataOps is to create software with software development teams being a secondary priority while the primary focus in DevOps is software developers, and operations professionals are secondary.

There’s an expectation of using automation tools for software development and software deployment in a DevOps environment. At the same time, DataOps is focused on using data science to extract valuable insights from the data.

Testing: software development vs. software deployment

In DevOps, the software is viewed as an important part of an organization’s assets. Within DataOps, the software is treated more like raw data, and it will be analyzed to extract information or used in different ways that can help create better software products for users.

Testing within the context of DevOps means conducting software tests while the software is being developed. Within DataOps, software testing means analyzing software as data after it has been completed in order to determine if it fulfills the requirements or not. 

Software developers are secondary in a DevOps environment, and they respond by providing necessary information for software deployment. Within Data Ops, software development teams are secondary, and data science teams respond by providing software developers with the results of software testing in order to meet requirements.

Software deployment within a DevOps context is more about software quality and software operations, while DataOps it’s more focused on analyzing data sets or existing software products that can be used as part of new software development projects.

In DataOps, data science is used for software development. In addition, software developers are expected to analyze results from software tests to meet requirements or develop new software products that will fulfill those requirements.

Tools: software development and software deployment

DevOps’ software developers work to onboard software as quickly as possible; focusing on quality is a priority. On the other hand, DataOps work involves using data scientists that will help determine how software should be developed based on data sets or software products that already exist.

In DevOps, there’s an expectation of using automation tools for software development and software deployment. On the other hand, DataOps is focused on using data science to extract valuable insights from the data.

DataOps aims to create software products that are more useful for users than existing software applications. Besides, DataOps works to onboard software as quickly as possible for users, prioritizing quality. On the other hand, DevOps work involves using software developers that will help determine the software that should be developed based on data sets or software products that already exist.

In DataOps, there’s an expectation of using automation tools for software development and software deployment while DevOps is focused on software developers, operations professionals are secondary. In a DevOps environment, the focus is software management with the goal to shorten the feedback loop and increase software quality. 

Additionally, DataOps software is viewed as an important part of an organization’s assets. On the other hand, DevOps focuses more on software developers with the goal to shorten the feedback loop and increase software quality.

What Are The Key Similarities Between DataOps and DevOps?

Both data scientists and software developers must communicate with each other to create software that benefits both the software development team and customers. Software developers as well are responsible for creating new features, fixing bugs within software applications, and ensuring software is delivered according to schedule. 

Data scientists focus on working alongside software developers so they can provide data-driven recommendations based on software products being developed. Moreover, Data scientists have the necessary data and experience to help software developers decide how software should be built, while software developers must prioritize customer needs before anything else as they build new features that will benefit customers in a variety of ways.

Some of the important DevOps concepts adopted by DataOps include:

• Focus on providing business value
• Agile development
• Focus on delivering business value
• Code promotion and automated testing
• Continuous delivery and continuous integration (CI/CD)
• Automation and Reuse 

Final Thoughts

DataOps software testing is used to help extract valuable insights from existing data sets or software products that already exist. Software development teams are secondary in a DevOps environment while central within DataOps software testing and deployment. Software quality doesn’t play as big a role in the software development process, but it’s crucial in software testing and deployment during DataOps.