An approach for preventing your code from getting too complex to navigate — Creating dataframes is easy, but managing the codebase to do so can become difficult and complex. When a data science team reached out for help to bring more structure to the problem, the Stitch Fix Platform Team worked together to build Hamilton [open source code]! …

A quick walkthrough of using these two open source projects together — In this post, we’re going to show you how easy it is to run Hamilton inside a DBT task. Making use of DBT’s exciting new python API, we can blend the two frameworks seamlessly. Hamilton is a declarative micro-framework to describe dataflows in python. For example, it’s excellent for expressing…

Five lessons learned while building platforms for Data Scientists. — Why build a platform? Picture this. You’re an individual contributor working at some company that requires you to write “code” to get your job done. I’m trying to cast a wide net here, for example, you could be a full-stack data scientist at Stitch Fix creating models that plug back into the business, or…

Scale the human side of your data transforms as well as the compute side — Hamilton is an open source, declarative dataflow micro-framework for Python. In this post I will explain what’s required to scale the data and CPU you use for your workflow with Hamilton by utilizing Hamilton’s Ray integration. This post assumes prior familiarity with what Hamilton is. …

Hamilton is an open source, declarative dataflow micro-framework for Python. In this post I’ll attempt to quickly explain what it is and how to use it with Pandas; reading this post shouldn’t take you more than five minutes. …

For those that don’t know, Hamilton is a general purpose micro-framework for specifying dataflows, e.g. specifying Pandas transforms. It helps you to structure your code base, and improves your code, e.g. you always write unit testable transform code with Hamilton. It does this by introducing a paradigm where functions have…