How a DataOps pipeline can support your data
March 31, 2021
DataOps has created a lot of hype as a data management pipeline because of its focus on collaboration and flexibility. Read on to find out how these priorities support your data.
A DataOps pipeline is an Agile framework that many enterprises have adopted to better manage their data. It provides a backbone for streamlining the lifecycle of data aggregation, preparation, management and development for AI, machine learning and analytics. It promises substantial improvement to traditional approaches to data management in terms of agility, utility, governance and quality of data-enhanced applications. The core idea lies in architecting data processes and technology to embrace change.
“DataOps brings a software engineering perspective and approach to managing data pipelines similar to the trend created in DevOps,” said Sheel Choksi, solutions architect at Ascend.io, a data engineering company.
Traditional data management approaches focused on schema changes, but Choksi emphasized the importance of also including shifting business requirements, delivering for new stakeholders and integrating new data sources.
DataOps pipeline planning needs to address automated tools that support quick-change management with version control, data quality tests and continuous integration/continuous delivery pipeline deployment. This can enable a more iterative process that increases a team’s output while decreasing overhead.