I hate chasing down why that dashboard looks off. You hate chasing down why that dashboard looks off. It always starts the same way: a stakeholder pings with a screenshot, something looks broken, and you find yourself deep in someone else’s query trying to track down what changed, when, and why. Meanwhile, trust in your […]
How Storing Dashboards as Code Increases Trust in Your Data Team Read More »
With the recent release of dbt 1.1, users may start to see warnings like this: Warning: while parsing model config: Ignore unexpected key “meta” dbt 1.1 introduces a subtle but important shift in how configuration properties are structured for sources, models, and other resources. What’s Changed? Historically, dbt allowed you to set properties like freshness,
Avoiding Config Warnings while Updating Your dbt Project to v1.1 Read More »
Recently, I participated in the dbt Fantasy Football Data Modeling Challenge, hosted by Paradime & Lightdash. Over 300 SQL and dbt experts tackled real NFL and fantasy football data. My approach, which placed 2nd overall, focused on building a self-service data mart for dynamic exploration of scoring trends and player performance. I used dbt for
Building a Data Mart with dbt, Lightdash, and Paradime Read More »
Organizing a business intelligence stack goes beyond pipelines and dashboards. It requires clear, accessible documentation. Tools like Lightdash, integrated with DBT, make this possible by combining robust ETL processes with a documented semantic layer. This ensures your data is understandable and actionable. In this post, we explore the concept of a semantic layer, its role
