DevOps and DataOps are two critical processes in testing, but it is essential to understand the distinction between the two. For years, DevOps has been the widely accepted practice for optimizing product delivery. The DevOps process combines the functions of engineering teams, information technology (IT), and development methodology to reduce the time and costs surrounding the development and release cycle.
Unlike other methods where separate teams remain separate, DevOps emphasizes collaboration to deliver a reliable product. These teams will often rely on automation tools to support essential concepts like continuous integration and continuous delivery (CI/CD).
While DevOps focuses on eliminating obstacles with a quality development cycle, DataOps extracts the data for actionable insights. DataOps is an automated, process-oriented methodology, used by analytic and data teams, to improve the quality and reduce the cycle time of data analytics. While DataOps began as a set of best practices, it has now matured to become a new and independent approach to data analytics.
Both practices maintain collaboration to ensure quality and efficiency. DataOps builds on the concept of DevOps by adding data specialists to the collaborating group of teams. These analysts, developers, engineers and scientists ensure streamlined data flows and continuous use of data throughout an organization.
DataOps is the hub for collecting and distributing data, with a mandate to provide controlled access to systems of record for customer and marketing performance data, while protecting privacy, usage restrictions and data integrity – Gartner
We test, once you develop. We monitor, once you deploy.
There is a lot more to DataOps than just what is mentioned in the above equation. In the CI/CD pipeline, RightData can connect to any of the DevOps integration tools to support Continuous Delivery in Agile deployment methodology. Those days of once a year or twice a year software releases are long gone. With the Agile methodology in place, the release cycle times or “sprints” are becoming much shorter these days, even as short as daily releases or daily sprints. Weekly sprints and bi-weekly sprints are now much more common. In such fast-paced deployments, the software and data test automation have a significant role to support the continuous delivery. RightData’s DevOps integration can help QA teams to test and validate the data also in parallel to their software testing cycles.
The primary goal of DevOps is to remove barriers between teams to create high-level communication and collaboration. This collaboration can exist between developers and testers to create a quality product. In non-DevOps arrangements, developers often work without an understanding of quality assurance (QA) and operation limitations.
DevOps keeps QA testers and developers in communication at every stage in a project. This communication allows engineers to develop strategies with testing requirements in mind. Testing processes can run more smoothly, and time and money are saved with a more efficient QA procedure.
CI/CD uses automated workflows to release products more effectively and frequently. DataOps uses toolchains and workflow automation in a defined process to change data that enters the system and move it downstream for transformation, visualization, models and reports.
With data constantly changing and updating, the product is ready to be released at any moment with new features and fixed code. DataOps speeds up the analytics process while ensuring data is prepared for integration and delivery.
RightData is a leader in DevOps to DataOps testing tools. With DataOps automation testing, your team can ensure your processes are streamlined and equipped for CI/CD. With our solutions, we empower your company to take charge of your data. Our team operates on trust, respect, focus and commitment to deliver DataOps and DevOps testing you can count on. Book a free demo of RightData testing solutions today.