Post

Savannah Ostrowski
@savannah.dev

🐍 Calling all Python data engineers! I’m looking to chat with Python developers working in data engineering—whether you’re building pipelines, wrangling data, or just figuring things out as you go. Details 👇

13 reposts44 likes

Patrick O'Leary
@pjaol.bsky.social

Depends on many things, but generally - Airflow coordinator - Celery / Ray - Dataframes all the way down - Presidio - Spacy / sklearn / pydantic / sqlalchemy Not a fan of snowflake though many folks are. I don’t like the arch or putting my data & trust into their platform. But I’m an exception

Savannah Ostrowski
@savannah.dev

Curious to hear about: - Your project architecture and how you structure your workflows - The tools you rely on (and the ones you wish were better) - What you love about your current setup—and what frustrates you - [OPTIONAL] Whether you've ever evaluated Snowflake (and if so, what you thought)

Savannah Ostrowski
@savannah.dev

Just exploratory research! I work on Python developer experience at Snowflake and want to get a more diverse sample of developers to influence a strategy I’m working on. Early perspectives are always welcomed!!

Lucas
@qfoiluks.bsky.social

Basically: - depends, basically it is defined by the infra already available or in place. - spark, airflow, Kafka, managed infra on k8s, aws in general, databricks, polars etc. - I love managed infra on k8s, and huge spark clusters on databricks ephemeral instances. - never tried, it's in my list.