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Published: 31 october summary to relieve bottlenecks and barriers in delivery of data and analytics solutions, organizations need to change the way they work. Data operations ( dataops) is the orchestration of people, process and technology to deliver trusted, high- quality data to data citizens fast. Introduction dataops is the new black, providing a combination of devops and agile practices to automate the creation and management of data, analytics, and visualization on cloud platforms. With complex pipelines, this can be challenging, making orchestration and automation key requirements in any dataops implementation.
A key role of dataops is to orchestrate and automate the flow of data and code between people and tools in an efficient manner that ensures clean handoffs and minimal errors and disruptions. Where did dataops come from? The benefits of dataops. Com data science and machine learning in the enterprise the promise of leveraging data for competitive advantage looms large in the modern enterprise.
Dataops: an agile methodology for data- driven organizations crystal valentine, phd, vp technology strategy, mapr william merchan, chief strategy oficer, datascience. The practice is focused on enabling collaboration across an organization to drive agility, speed and new data initiatives at scale. Dataops is revolutionizing the way we deploy and manage data platforms. By introducing dataops techniques in a focused manner, data and analytics leaders can affect a shift toward more rapid, flexible and reliable delivery of data pipelines. Dataops builds on concepts popular in the software engineering field, such as agile, lean, and continuous integration/ continuous delivery, but addresses the unique needs of data and analytics environments, including the use of multiple data sources and varied use cases that range from data warehousing to data science.
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