| Website | https://paulzuradzki.com |
| GitHub | https://github.com/paulzuradzki |
I gave a talk at Chicago Python on "Dependency Injection for Pythonistas" a few weeks back and decided to make it a written tutorial style post. It's my first time publishing in this format. Feedback welcome!
https://paulzuradzki.com/2025/7/dependency-injection-for-pythonistas/
"Vendoring" by Carson Gross
https://htmx.org/essays/vendoring/
"You get more of what you make easy, and if you make dependencies easy, you get more of them."
"This demonstrates significant cultural problem with dependency managers:
They tend to foster a culture of, well, dependency."
Carson Gross explores the concept of 'vendoring' in software development, where external project sources are copied directly into a project. He covers the benefits of vendoring, such as improved visibility and control over dependencies, and discusses challenges like transitive dependencies and the culture of dependency in modern software development. He also contrasts vendoring with modern dependency management tools, and considers the potential for vendor-first dependency managers to combine the strengths of both approaches. He encourages a rethinking of dependencies and promotes a more independent approach to software development.
Opposite of Cloud Native is?
It's time to define what it means to be NOT cloud-native, in a positive way. #python
https://mkennedy.codes/posts/opposite-of-cloud-native-is-stack-native/
(continued...)
- Data architecture patterns like Medallion Architecture: https://www.databricks.com/glossary/medallion-architecture
- Standardization and cleaning.
When we use Python vs SQL. Pydantic for data validation: https://docs.pydantic.dev/latest/
- Data profiling and schema inference tools.
- Capital One Data Profiler: https://www.capitalone.com/tech/open-source/basics-of-data-profiler/
- Snowflake INFER_SCHEMA: https://docs.snowflake.com/en/sql-reference/functions/infer_schema
- Data privacy and data deletion policies. Propagating deletion to downstream data.
Some themes that came up in data engineering open space:
- Data modeling and semantics (e.g., metric definition or unique identifier definition)
- OMOP Common Data Model for Electronic Medical Records: https://ohdsi.github.io/CommonDataModel/
- Tuva Health Knowledge Base and Open-Source data model for administrative claims and enrollment: https://thetuvaproject.com/
- Managing tension of schemas being efficient for back-end ops vs user-friendly
Hello from #PyConUS "Data Engineering Patterns for Healthcare/XYZ Sector" Open Space 👋🧑🔧🐍
Open Space attendees: feel free to Like/Comment to find each other here and share any notes. I'll post my notes here too.
Konrad Hinsen
Vicki Boykis
Michael Kennedy
David Lord 
Mike Fiedler, Code Gardener