4 min read
dbt, Spark, or just a Python script?
A decision guide for the question every data team argues about, based on data volume, team size, and how much operational burden you can actually carry.
Tag
4 pieces tagged “engineering”, newest first.
A decision guide for the question every data team argues about, based on data volume, team size, and how much operational burden you can actually carry.
The idea is sound and the tooling around it has become theatre. Here is the version that fits on one page and actually prevents incidents.
Timezones, DST, event time versus processing time, and the specific bugs each produces. This is the topic that costs the most engineering hours per unit of conceptual difficulty.
CSV, JSON, Parquet, Arrow, and the table formats layered on top. What each is actually for, and the specific ways choosing wrong costs you.