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What exactly is DataOps ? The term has been used a lot more of late, especially in the data analytics industry, as we’ve seen it expand over the past few years to keep pace with new regulations, like the GDPR and CCPA. In essence, DataOps is a practice that helps organizations manage and govern data more effectively.
The audience grew to include datascientists (who were even more scarce and expensive) and their supporting resources (e.g., ML and DataOps teams). After that came data governance , privacy, and compliance staff. Power business users and other non-purely-analytic data citizens came after that.
Consequently, AIOps is designed to harness data and insight generation capabilities to help organizations manage increasingly complex IT stacks. MLOps platforms are primarily used by datascientists, ML engineers, DevOps teams and ITOps personnel who use them to automate and optimize ML models and get value from AI initiatives faster.
Integrating helpful metadata into user workflows gives all people, from datascientists to analysts , the context they need to use data more effectively. The Benefits and Challenges of the Modern Data Stack Why are such integrations needed? Before a data user leverages any data set, they need to be able to learn about it.
People come to the data catalog to find trusted data, understand it, and use it wisely. Today a modern catalog hosts a wide range of users (like business leaders, datascientists and engineers) and supports an even wider set of use cases (like data governance , self-service , and cloud migration ).
For some time now, data observabilit y has been an important factor in software engineering, but its application within the realm of data stewardship is a relatively new phenomenon. Data observability is a foundational element of data operations (DataOps).
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