DataOps is a set of practices and technologies that operationalizes data management, collaboration, integration, and automation to ensure resiliency and agility despite constant change. It combines the DevOps principles of continuous delivery and the ability to harness data drift (unexpected and undocumented changes to data).
Core to delivering on DataOps practices is a modern data integration platform that provides users speed, flexibility, resiliency, and reliability among data engineers, data scientists, and data analysts. DataOps systems embrace change by allowing their users to quickly adopt, understand and use complex new platforms to deliver the business functionality they need to remain competitive.
The benefits of DataOps
DataOps can accelerate delivery while improving data quality. Also, it can help to reduce the cycle time of data analytics in close alignment with business objectives. DataOps is not just DevOps for data, as DataOps handles the complexity of data due to the constant rate of change called Data Drift.
DataOps can speed up the entire data analytics process by providing real-time data insights. DataOps moves code and configuration continuously from development environments to production, leading to near real-time data insights.
DataOps can embrace Data Drift and assume that data, semantics, and infrastructure will change. DataOps pipelines and processes are loosely coupled and flexible to schema variations.