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In early April 2021, DataKItchen sat down with Jonathan Hodges, VP Data Management & Analytics, at Workiva ; Chuck Smith, VP of R&D Data Strategy at GlaxoSmithKline (GSK) ; and Chris Bergh, CEO and Head Chef at DataKitchen, to find out about their enterprise DataOps transformation journey, including key successes and lessons learned.
DataOps and DevOps are two distinctly different pursuits. But where DevOps focuses on product development, DataOps aims to reduce the time from data need to data success. At its best, DataOps shortens the cycle time for analytics and aligns with business goals. What is DevOps? What is DataOps?
DataOps Automation (Orchestration, Environment Management, Deployment Automation) DataOps Observability (Monitoring, Test Automation) Data Governance (Catalogs, Lineage, Stewardship) Data Privacy (Access and Compliance) Data Team Management (Projects, Tickets, Documentation, Value Stream Management) What are the drivers of this consolidation?
Enter DataOps. What is DataOps? DataOps is an approach to data management that increases the quantity of data analytics products a data team can develop and deploy in a given time while drastically improving the level of data quality. But the approaches and principles that form the basis of DataOps have been around for decades.
Align your data modeling environment with development projects in support of DevOps and DataOps practices. with the new ER360 collaboration portal appeared first on erwin Expert Blog. Enhance the curation and association of business metadata with data models to further enable semantic integration and understanding.
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