We write about tools we use and decisions worth thinking through.
For business leaders thinking about data costs, team structure, and build vs buy decisions. No code required.
Hiring a data engineer is the obvious move when you want to become more data-driven. But the role comes with a real cost, a timeline, and a specific set of problems it solves. Here's how to think about the decision.
Read Article →Tools we actually use, tried in production, honest about the tradeoffs. Open source first.
After DLT for ingestion and DBT for transformation, you need somewhere to query the results. Most teams default to BigQuery or Snowflake. We chose MotherDuck. Here's why.
Read Article →DBT turns raw data into clean, documented, version-controlled models. We use it to make analysts independent from engineering. Here's how it works and why we chose it.
Read Article →DLT is a Python library for building data pipelines. It's free, open source, and runs anywhere. We chose it over Fivetran after comparing the costs and tradeoffs directly.
Read Article →We help you prove value before scaling.