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).
I spend 8+ hours a day working with/ingesting data. Had no clue. Checked with my data-using colleagues just to see if any of them had heard of Arrow - about 50% had. These are people who spend their entire day working/ingesting data in various pipelines. So, while I won't comment on whether the subtitle would be useful, I'm pretty confident that the (perhaps not large) majority of people who would/could use Apache Arrow had not hear of it.
A lot of people just stay in their lane. Having a solid description, like the one you provided, would be super useful. Perhaps a tool-tip feature of HN, even.
This is just a mismatch with how HN posts pages and how projects expect their release pages to be read. Release pages are for users to know what changed. But HN likes to link to original sources and not articles about the source. In cases like this, we end up pretending the release page is meant to be read by a wider audience than the writers ever intended it for - not Apache's fault! Apache Arrow's homepage does have that one-sentence description on it ('A cross-language development platform for in-memory analytics').
A blog post announcing version 1.0 is a good opportunity for a one-liner to explain what the heck your project is :) Just in case your audience happens to be expanding instead of contracting...
Apache Arrow? First thing the top of my head(given that I had to work with this these past few days), the python snowflake connector depends on it if you intend to fetch the results from snowflake as a pandas or dask dataframe. In other words - lots of places.
(contributor here) At my company our proprietary database layout is based on Arrow. Our workload is analytical and we are using it for read mostly workloads.
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/