On the joy of inheriting a rather bad dataset - dissecting ~120GB of terrible Google Takeout data to make it usable, using Dataflow/Beam, go, Python, and SQL.
In Part 2 of our comparison of Python and go from a Data Engineering perspective, we'll finally take a look at Apache Beam and Google Dataflow and how the go SDK and the Python SDK differ, what drawbacks we're dealing with, how fast it is by running extensive benchmarks, and how feasible it is to make the switch
Exploring golang - can we ditch Python for go? And have we finally found a use case for go? Part 1 explores high-level differences between Python and go and gives specific examples on the two languages, aiming to answer the question based on Apache Beam and Google Dataflow as a real-world example.