DataOps Concepts

DataOps is a methodology to build and maintain data analytics and data science systems that reduce cycle time and increase quality over traditional development and operations methods.

DataOps uses a series of principles that combine the concepts of Agile, DevOps, and Lean Manufacturing to support innovation with low error rates across heterogeneous teams, tech, and environments.

DataKitchen believes there are seven key practices that analytic teams must follow to implement DataOps and adhere to DataOps Principles.

DataOps practices

1. Orchestrate: orchestrate your production pipeline.
2. Monitor: monitor production data for errors and trends.
3. Use multiple environments: use multiple self-service environments to experiment outside of production.
4. Add tests: add automated data and logic tests to catch problems quickly.
5. Reuse and containerize: reuse and containerize components to save time and reduce complexity.
6. Parameterize: parameterize your code so it can run in multiple environments.
7. Schedule: schedule pipeline processing for regular, predictable deliverables.

Helpful resources

• The DataOps Maturity Model
• What is DataOps?
• What is DataOps Observability?
• DataOps Observability: Taming Chaos