What is DataOps?
To understand what DataOps is, take a look at DevOps.
The term DevOps was coined around 2010 and is a portmanteau that combines development and operations into a single term. It’s a set of practices and tools to integrate the process of developing and deploying software.
The advent of DevOps was facilitated by several organizational and technological evolutions:
- agile project methodology and working on the highest priorities in short iterations;
- cloud computing and on-demand deployment of computing services enable software developers to deploy their own infrastructure;
- infrastructure-as-code (IaC) and the provisioning of identical environments make collaborating on the same project within the same conditions less burdensome.
DevOps integrates processes between software development and IT teams. It’s meant to deliver applications at better speed and quality. Several principles are at its core:
- collaboration between development and operations;
- automation of the development lifecycle;
- continuous improvement.
Apply the same goals, principles, and evolutions to data products: enter DataOps. It is a method to deliver high-quality data products at high velocity. These goals are achieved through:
- close collaboration of data scientists, analysts, data engineers, analytics engineers, IT, and quality assurance;
- automation throughout the end-to-end analytical process;
- continuous improvement and integration of new features into data products.
If setting up an automated dashboard or a recurring report within your organization is cumbersome, it’s probably because the development workflow of data products isn’t streamlined and the stakeholders involved aren’t on the same page. DataOps could be an enabler.