# How we model our data warehouse

DevFeed: [How we model our data warehouse](<https://devfeed.tech/articles/how-we-model-our-data-warehouse-11816.md>)

Original publisher: [Read original article](<https://incident.io/blog/how-we-model-our-data-warehouse>)

Author: Jack Colsey

Published: 2024-11-08T16:31:00Z

Content type: article

Language: en

Sources: [The incident.io Blog](<https://devfeed.tech/sources/the-incident-io-blog.md>)

Topics: [data-architecture](<https://devfeed.tech/topics/data-architecture.md>), [data-modeling](<https://devfeed.tech/topics/data-modeling.md>), [data](<https://devfeed.tech/topics/data.md>)

Tags: [blog](<https://devfeed.tech/tags/blog.md>), [blog-post](<https://devfeed.tech/tags/blog-post.md>), [data](<https://devfeed.tech/tags/data.md>), [data-modeling](<https://devfeed.tech/tags/data-modeling.md>), [incident](<https://devfeed.tech/tags/incident.md>), [incident-channel](<https://devfeed.tech/tags/incident-channel.md>), [incident-management](<https://devfeed.tech/tags/incident-management.md>), [incident-response](<https://devfeed.tech/tags/incident-response.md>), [model](<https://devfeed.tech/tags/model.md>), [outage](<https://devfeed.tech/tags/outage.md>), [post-mortem](<https://devfeed.tech/tags/post-mortem.md>), [salesforce](<https://devfeed.tech/tags/salesforce.md>), [slack-incident](<https://devfeed.tech/tags/slack-incident.md>), [stripe](<https://devfeed.tech/tags/stripe.md>)

## AI overview

This blog post explains incident.io's data warehouse design and data-modeling principles. It describes a layered structure based on dbt's approach, using staging, intermediate, and marts layers, with guidance on renaming columns, casting data types, joining tables, performing reusable calculations, and organizing customer-facing and internal data models. It also discusses how these principles support flexible but consistent modeling in a BI tool and how intermediate calculations can improve pipeline performance.

## Source excerpt

Curious about the inner workings of our data warehouse? We've shared a lot about our data stack, but this time we're diving into the design principles behind our warehouse. This blog breaks down how we structure our data, from staging to marts layers, and how we use it all in our BI tool. It's a quick look into how we keep things flexible, efficient, and built to scale.