DataOps: Industrializing Data and Analytics
DataOps is an emerging set of practices, processes, and technologies for building and enhancing data and analytics 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. The goal is to increase agility and cycle times, while reducing data defects, giving developers and business users greater confidence in data analytic output.
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. It relies heavily on test automation, code repositories, collaboration tools, orchestration frameworks, and workflow automation to accelerate delivery times while minimizing defects.
DataOps requires cultural shift. It is not something that can be implemented all at once or in a short period of time. DataOps is a journey. Leaders use productivity metrics to gauge their progress and impel them and their teams to continuously search for new ways to cut wasted effort, streamline steps, automate processes, increase output, and get it right the first time. For large organizations with big development teams, DataOps is an antidote to many of the woes that beset IT and development organizations.