DevOps and DataOps are two distinctly different pursuits. Both are based on Agile methodology that is designed to accelerate working cycles. But when DevOps focuses on product development, DataOps aims to reduce the time from data needed to data success. Both DevOps and DataOps are crucially cross-functional in the development of the business.
1. What is DevOps vs. DataOps?
DevOps
Development Operations, or DevOps, combine engineering-side product development and operational-side product delivery. Operations provide information to Development when new or increased development is required, and Development then plans its creation. To lower the cost of product development and speed up release cycles, DevOps brings together several teams. Along with enabling quicker, more effective innovation, removing the barriers separating the Engineering, Software Development, Quality Assurance, IT Operations, and other teams can enhance the scale and boost security and reliability. Modern enterprises now provide software in only minutes instead of months thanks to the DevOps methodology.
DataOps
Meanwhile, Data Operations, or DataOps, is intended to provide high quality data and analytics solutions at a rate that is both faster and more reliable over time. Agile development, lean manufacturing and statistical process control are the fundamental manufacturing approaches that served as the foundation for DataOps. Finding the right data for the right application fast is the goal of data operations. To meet the business requirement for insights, it brings together business users, data scientists, data analysts, IT, and application developers. In order to accomplish business objectives, DataOps then works to continuously improve and modify data models, visualizations, reports, and dashboards.
2. DevOps vs. DataOps: The workflow
3. DevOps vs. DataOps: Skills and Team requirements
| Skills | Teams | |
|---|---|---|
| DevOps |
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| DataOps |
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4. DevOps vs. DataOps: Key takeaways
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Output: DevOps delivers a quality product; DataOps utilizes high-quality data to make high quality outputs.
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Collaboration: DevOps works with engineering and development teams; DataOps works with business users, application developers, and IT operations.
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Cycle times: DevOps strives for shorter release cycles to meet business demands; DataOps strives to build a continuous data pipeline so business users become self-sufficient.
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Operations: DevOps runs repeatable, highly similar cycles; DataOps is constantly addressing new and changing data challenges involving many sources and needs.
Final thought
DataOps focuses heavily on the transformation of intelligence systems and analytic models by data analysts and data engineers, whereas DevOps is the transformation of the delivery capability of development and software teams. Two functions are essential to all business, especially in the digital world.
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