# Data engineering · Glossary
The discipline of building and operating the systems that collect, move, transform and store data so it can be used reliably for analytics, applications and AI.
[Glossary](/glossary) · Data platforms and engineering

# Data engineering

     The discipline of building and operating the systems that collect, move, transform and store data so it can be used reliably for analytics, applications and AI. It covers pipelines, platforms, data models, quality controls and operational monitoring.

## Why it matters

     Most of the effort in analytics and AI is data engineering. Without dependable pipelines and well-structured data, analysts spend their time reconciling numbers and AI projects stall at the data preparation stage.

     Good data engineering is judged by how the platform behaves over time: whether it can be changed safely, whether failures are detected quickly and whether the client’s team can run it without the people who built it.

## In practice

     For example, a UK multi-academy trust might need data engineering to bring attendance, assessment and finance data from separate school systems into one governed platform, refreshed daily, before any trust-wide reporting or forecasting is possible.

## Where Rodan fits

     Rodan’s [Data and Analytics Engineering](https://rodan.io/data-analytics-engineering) discipline builds data foundations that teams can own. See also [how to evaluate your current data infrastructure before investing in AI](https://rodan.io/insights/how-to-evaluate-your-current-data-infrastructure-before-investing-in-ai).

## Related terms

- [Data pipeline](/glossary/data-pipeline)

- [Data warehouse](/glossary/data-warehouse)

- [Analytics engineering](/glossary/analytics-engineering)

- [Data architecture](/glossary/data-architecture)

- [Data quality](/glossary/data-quality)

- [Data maturity](/glossary/data-maturity)
HTML: https://rodan.io/glossary/data-engineering