DataOps applies DevOps principles to data analytics and data engineering, creating a collaborative, automated, and iterative approach to developing, deploying, and maintaining data pipelines and analytics workstreams. Drawing from the DataOps Manifesto and its 18 principles, DataOps emphasizes continuous collaboration between data engineers, analysts, and business stakeholders, treating data pipelines as production-grade software with rigorous testing, version control, and deployment automation. In 2026, DataOps has evolved beyond simple automation to include AI-driven observability, data contracts, and self-healing pipelines, enabling organizations to deliver reliable, high-quality data at the speed business demands while maintaining governance and compliance.