Inside Analysis

Best Practices in DataOps: How to Create Agile, Automated Data Pipelines

DataOps is an emerging set of practices, processes, and technologies for building and automating data pipelines to meet business needs quickly. As these pipelines become more complex and development teams grow in size, organizations need better collaboration and development processes to govern the flow of data and code from one step of the data lifecycle to the next – from data ingestion and transformation to analysis and reporting.

DataOps is not something that can be implemented all at once or in a short period of time. DataOps is a journey that requires a cultural shift. DataOps teams continuously search for new ways to cut waste, streamline steps, automate processes, increase output, and get it right the first time. The goal is to increase agility and cycle times, while reducing data defects, giving developers and business users greater confidence in data analytic output.

This webcast examines how organizations adopt DataOps practices in the field. It will review results of an Eckerson Group survey that sheds light on the rate and scope of DataOps adoption. It will also describe case studies of organizations that have successfully implemented DataOps practices, the challenges they have encountered and benefits they’ve received.

Tune into our webcast to learn:

– User perceptions of DataOps
– The rate of DataOps adoption by industry and other demographic variables
– DataOps adoption by technique and component (i.e., agile, test automation, orchestration, continuous development/continuous integration)
– Key challenges organizations face with DataOps
– Key benefits organizations experience with DataOps
– Best practices in doing DataOps
– Case studies and anecdotes of DataOps at companies

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