Week 24/34: DevOps and DataOps Practices for Data Engineering Interviews
Building trustworthy, observable, and resilient data systems with the right mindset, culture, and tools.
DevOps is more than just a set of tools like Docker, it's a mindset and a way of working. It emphasises collaboration, shared responsibility, and a strong team culture. As highlighted in Effective DevOps: Building a Culture of Collaboration, Affinity, and Tooling at Scale1 by by Jennifer Davis and Ryn Daniels, the true power of DevOps comes from how people and tools work together to enable safe, fast, and reliable change.
DevOps is a way of working where developers and operations teams work together instead of separately. In the past, developers would build software, then hand it off to the IT team to run, often causing delays and problems. DevOps fixes this by making them one team, using tools like automation and version control to release software quickly and safely.
DataOps takes the ideas of DevOps and applies them to data work. In data engineering, we're not just writing code; we’re moving and transforming data from many sources. DataOps helps teams plan, build, test, and release data products in a fast and reliable way.
In data engineering, especially where it overlaps with software development, applying DataOps principles is a game-changer. It not only prevents costly errors and improves reliability, but also shows we’re thinking beyond just building pipelines; we’re thinking about building trustworthy, maintainable systems.
In this post, we’ll cover:
What is DataOps?
Three pillars of DataOps
How DataOps fits into the Data Engineering Lifecycle.
Key DevOps/DataOps tools every Data Engineer should know.
interview questions, including hands-on scenarios involving Docker, CI/CD, and Terraform
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Here is the full plan:
What is DataOps?
Problems are inevitable, whether a server crash, a cloud outage, buggy code, or bad data. How we manage data, from collection to action, matters more than ever.
DataOps is the discipline of applying DevOps, Agile, and Lean principles to data workflows. It focuses on delivering high-quality, trustworthy data products, like dashboards, machine learning models, and reports, faster and more reliably.
While software delivers features, data delivers insight. That makes collaboration, testing, monitoring, and continuous delivery just as critical in data engineering.
Core ideas behind DataOps:
1. Culture over tools
While automation and monitoring tools are essential, DataOps is fundamentally about breaking down silos between data engineers, analysts, scientists, and business stakeholders. It's about creating a culture of collaboration, shared responsibility, and continuous learning.
2. Data products, not just data
Raw data has no value until it becomes actionable insights. DataOps focuses on delivering complete data products: dashboards, ML models, reports, that solve real business problems and provide measurable value.
3. Continuous Everything
Just as DevOps emphasises continuous integration and deployment, DataOps champions continuous data quality, continuous monitoring, and continuous improvement of data workflows.