However, the data science/big data world has been bogged down by inefficient, clunky formats like Parquet for quite a while and so I applaud any steps towards bridging the gap between the two worlds.
However, the data science/big data world has been bogged down by inefficient, clunky formats like Parquet for quite a while and so I applaud any steps towards bridging the gap between the two worlds.
Generally with “data stuff” you’re chained to whatever the dominant language and tools do (for the longest time, Python and Spark) and you’d have to accomodate them and their idiosyncrasies directly. Nowadays though, basically everything has a pq lib, there’s tools at all scales: DuckDB, ClickHouse, Databend, etc. The Arrow internal representation of parquet means we’ve escaped the dominance of single massive packages (e.g. pandas and spark dataframes) as the only way to deal with certain datasets.
I think it’s great what we’ve managed to get via Parquet, even if it’s a little bit clunky.
What I’m very much hoping is things stay in the current style of “mostly” language independent formats and tools. I’ve written more than enough Python and Scala/Spark and I’ve moved on that I don’t ever want to go back to the abysmal landscape that was “Python or bust”.