Ask HN: Data engineers, What suck when working on exploratory data-related task?

Related to this is obtaining data in bulk - teams (understandably) are usually not willing to hand out direct read access to their databases and would prefer you use their API, and they've usually built APIs intended for accessing single records at a relatively slow rate. It often takes some convincing (DoSing their API) to get a more appropriate bulk solution.

- Things that prevent you from starting the job. Org silos, security, and permissions

- Things that prevent you from doing the job. This is primarily data cleaning.

- Things that make the job more difficult. This involves poor tooling, and you'll struggle to break the stranglehold that SQL and python-pandas have in this area. I'll also add plotting libraries to this. Many of them suck in a seemingly unavoidable way.

On the second and third points llms will most likely own these soon enough, though maybe there's room to build something small and local that's more efficient if the scope of the agent is reduced?

The first point is organizational generally, and it's very difficult to solve outside of integrating your system into an environment which is the strategy pursued by companies like snowflake and databricks.