DataOps promises to streamline the process of building, changing, and managing data pipelines. Its primary goal is to maximize the business value of data and improve customer satisfaction. It does this by speeding up the delivery of data and analytic output, while simultaneously reducing data defects—essentially fulfilling the mantra “better, faster, cheaper.”
DataOps emphasizes collaboration, reuse, and automation, along with a heavy dose of testing and monitoring. It employs team-based development tools for creating, deploying, and managing data pipelines. This report explains what DataOps is, where it came from, what it promises, and how to apply it successfully.
Download this research report to learn:
- DataOps applies the rigor of software engineering to data development.
- DataOps practices borrow from DevOps, Agile, Lean, and Total Quality Management (TQM) methodologies.
- DataOps makes it possible to scale development and increase the output of data teams while simultaneously improving the quality of data output.
- The core mantras of DataOps are: faster, better, cheaper; collaborate, iterate, automate; and standardize, reuse, refine.
- DataOps requires a culture of continuous improvement.