Key differences in uses of DataOps vs. DevOps
DevOps is a collaboration between the application development and IT operations teams. The emphasis of this approach is communication and cooperation between these teams, and it has bled into data operations in a process called DataOps.
Both approaches signify that collaboration is the overriding approach to application development and IT operations teams, but they are targeted at different areas of an enterprise.
The idea of DevOps is far from a new concept. Merging application development and IT operations has become a standard in the enterprise and marks an important moment in the maturity of a business. DevOps aims to improve communication and collaboration across the two teams, but organizations have taken this a step further and spread the DevOps model across their business, focusing on eliminating silos and on cooperation among departments. “Companies wanted to build solutions for market and shorten the time to value and improve the quality of the products that were coming to market from a technical perspective,” said Michele Goetz, principal analyst at Forrester. This is a broad interpretation of the team, however. In a narrower definition, DevOps describes the adoption of iterative software development, automation and programmable infrastructure deployment and maintenance. Organizations that embrace the broad DevOps approach have common methodologies. Continuous integration (CI) tools, as well as continuous delivery (CD) tools, for the purposes of task automation appear in DevOps. Development teams incorporating CI see shorter and less disruptive code integration and improved bug detection. Examples of CI/CD tools include Jenkins, an open source automation server, and the GitLab platform. Turning to DevOps leads organizations to real-time monitoring, incident management systems, configuration management and collaboration platforms.
The philosophy of greater collaboration has led to DataOps. This is an Agile approach to designing and implementing a data architecture that supports open source tools and frameworks in production. Essentially, DataOps has the goal of getting business value from big data. What DevOps and DataOps have in common is their commitment to breaking down data silos and focusing on communication across teams. For Goetz, DataOps is a subset of DevOps, and it includes the members of an organization that deal with data: data scientists, data engineers and data analysts. So, it is not necessarily DataOps vs. DevOps as much as one complements the other. “It’s those team members who know how to operationalize data and operationalize models at scale within these digital and intelligent solutions that we’re working with right now,” Goetz said. DataOps focuses on IT operations and software development teams and only works if line-of-business stakeholders work with data engineers, data scientists and data analysts.
