DataOps is not feasible without automation. Highly automated and augmented data pipelines will only deliver faster data enablement.
As data pipelines grow in number and size, the organizations need to set some standards to govern data at various stages in the pipelines. Standardization and repeatability are the core components of automation. The organization that implements automation is more impregnable to schema drifts and changes in data.
Automated continuous testing is essential in building trust in data. Thousands of test cases can be generated automatically for data pipelines and can be used to test data continuously. The tests are simple and addictive. Whenever a change is made to data pipelines, test cases are created in DataOps. These tests are the early warning indicators of data quality issues.
As the complexity of data pipelines rises, the interconnections in the data elements also become complex and the pipelines are prone to more errors. Automated continuous testing can help boost confidence in data.
Further, statistical process controls to ensure continuous monitoring of the data pipelines by analyzing the output data. Any variations in data outputs can be identified, studied and appropriate action can be taken to resolve the issues.
All these practices of DataOps, if applied to the fullest, can reduce cycle time drastically allowing the business users to dive deep into the data without any waiting time. It also encourages a collaborative working environment and promotes agility.