What is DataOps?
According to Gartner, “Data Operationalization (DataOps) is a collaborative data management practice focused on improving the communication, integration, and automation of data pipelines within the organization. “The goal is to deliver value faster by creating predictable delivery and change management of data, data models, etc.
DataOps uses technology to automate the design, manage the deployment, and delivery of data with the appropriate levels of governance and metadata to enhance the use of data in a dynamic environment.
Why is DataOps important?
As data moves from source to consumption, at each step it is interpreted based on its use- for analytics, data science, and machine learning. This results in brittle pipelines that are incredibly slow to respond to change. DataOps helps with building, managing, and scaling data pipelines toward reusability, reproducibility, and rollback if required. The technology components supporting the management of different versions of the extraction, transformation, and loading (ETL) script could be in the form of an ETL store.
How does DataOps help?
DataOps represents a culture change that focuses on improving collaboration and accelerating service delivery by adopting lean and iterative practices to scale data pipeline operations from development to delivery. It can help provide the agility, efficiency, and continuous assessment of data throughout its life cycle. The goal is to provide data and infrastructure readiness by integrating siloed data sources. This in turn allows the creation of data pipelines with orchestration tools that can be provisioned automatically within production environments while ensuring governance and security across development and deployment environments.