Is DataOps and analytics stuck in the dark ages while transactional systems advance through modern DevOps practices?
It is evident that though the number of data projects exist down in dumps, it is difficult to add new features. Low level of precision and number of errors in data project reflects the need for better operational processes to enable quick feedback. From the outset, organizations have to be adept at integrating agility, speed and value. According to curated output of the crowd chat on this topic, DataOps is defined as a combination of data and engineering. It encompasses data flow management and integration at inception stages followed by ensuring data quality, security, privacy and overall governance …
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