Data pipelines are growing faster than ever because of AI and machine learning. By the end of this decade, they’ll be 3–4x bigger — but most current systems are not ready for this scale.

To keep up, DevOps, DataOps and database teams need to work as one. Unifying these workflows is the only way to get the visibility, feedback loops and continuous optimisation needed to keep modern data pipelines stable, secure and revenue-driving.

🔁 Back to DevOps Basics: Shift Left

Teams must return to core DevOps principles — strong collaboration, early feedback, and catching issues before they hit production.
As data pipelines blend with traditional development pipelines, these practices must also apply to data engineers, data scientists and BI teams.

🎯 Focus on Shared Goals

Even though developers, DBAs, data engineers and Ops teams have different roles, they all aim for the same outcome: high-quality, compliant and reliable data.
This means understanding how database changes affect analytics pipelines, ensuring compliance at every stage, and working with DBAs as shared governance partners.

👀 Observability Is Key

When dashboards break or reports look incorrect, observability helps teams track the issue — whether it’s a schema change, broken data flow or upstream error.
Without proper observability across DevOps and DataOps, there’s no trust in data.

🌐 A Holistic Way Forward

DataOps brings its own strengths — like testing full data pipelines in lower environments — and DevOps must integrate these practices.
Modernisation isn’t about choosing one approach; it’s about bringing both worlds together to support the rising demand for AI-driven data.

Source: https://devops.com/data-pipeline-modernization-in-2025-hinges-on-unifying-devops-dataops-and-database-workflows/