We’ve said it before, but it bears repeating: both DevOps and DataOps strive for the rapid and consistent delivery of exceptional code, procedures, and data. They also give their perspectives on what business values should look like, with the goal of eliminating silos, streamlining operations, and aligning teamwork. So, it stands to reason that both methods facilitate cooperation among individuals with various skill sets in order to create value and performance.
Several businesses have insufficient data proficiency, which leads to suboptimal judgments about datasets and applications. For this reason, the benefits of implementing DevOps capabilities to data are too compelling to ignore.
DevOps & DataOps: The Key Differences
DevOps & DataOps: The Key Similarities
The Power of the Duo.
To put it simply, DevOps manages code while DataOps oversees data. Yes, automation is important to both, but these discrepancies result in major variances as we progress through the automation pipeline. In other words, the essential purpose DevOps and DataOps serve are radically distinctive but that’s what makes them the power duo. Let’s explore it:
DevOps and DataOps adhere to the same lean techniques, concentrating on continuous improvement, customer demands (value), identifying and eliminating waste, and simplifying and standardizing processes. More in detail below:
DevOps applied to Data + Operations = DataOps
DataOps is a newer idea that is currently maturing in many ways. Because best practices and tools are always evolving. With that being said, it would be wise to copy and paste a few pages from DevOps into your DataOps action plan to derive its full potential.
This is where we step in and do it for you. We will combine the power of DevOps and DataOps through our tried-and-true ways to build data pipelines that empower your business.
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