Normally I would agree but Apache Arrow already has significant name-recognition to people who are likely to use it -- data engineers/scientists etc. It's a library that provides an in-memory columnar format, and supplies a data engine currently used in Apache Spark, Pandas, and libraries like turbodbc, which helps these tools achieve high performance on operations on tabular data.

Having a single high-performance in-memory format means different programs can read/write from the same source without serializing/copying/deserializing. For instance, if you wanted to pass a huge table of data from R to Java to Python (because your tools span different languages), normally you'd have to copy and serde (Protobuf? JSON?) to pass the data, which equals huge overheads. With Arrow, each of those languages can directly interact with the same copy of in-memory data, in-process -- with the highest possible performance.

You also get the performance of columnar databases without implementing your own columnar data structure.

But of course, no harm adding a short description to the title to broaden its audience. Arrow is truly something amazing and the more people know about it the better.[1] Folks who program against traditional databases might not know about it, and I think they should, especially if they need to generate analytics (i.e. fast filtering/aggregation for dashboarding or for data pipeline tasks).

[1] Overview: https://arrow.apache.org/overview/