Emily's Parkay butter pics made me laugh. Really enjoyed this. Great job Emily!!

Wow, great presentation!

Very informative and nicely articulated.

Great presentation, touched a lot of important areas, thanks

Fantastic talk! I wish there was a little more info on the format spec itself.

this is wonderful, enjoyed start to end :)

Great talk, Thank you

Awesome presentation!

AWS S3 supports random access queries (i.e., Range Header), consequently pushdown is also supported on AWS S3

Where are the slides?

Amazing speaker!

good explanations , this would be great if some git code they can mention

Great talk!!! I set up a spark-cluster with 2 workers. I save a Dtaframe using partitionBy ("column x") as a parquet format to some path on each worker. The matter is that i am able to save it but if i want to read it back i am getting these errors: - Could not read footer for file file´status ...... - unable to specify Schema ... Any Suggestions?

Eh so basically any sort of growing data can be only partitioned in one way (along the dimension of the growth - which for many use cases will be some meaningless "autoincrement" id). Which then defeats all the push-down filtering for any other dimension. Not to mention that if your data keeps growing in small increments and you need access to latest of it, you will have to jump through hoops to somehow integrate all those small increments into bigger files - because scanning 20000 tiny files ain't gonna be efficient (and this means lots of constant rewriting - that's why write speed DOES matter and it's not "write-once", but write-many)...

Great talk on a very important (and too often overlooked ) topic

Awesome thanks!

具有一定的指导学习作用。

to my experience, orc supports nesting...

The only thing I liked is the way Emily executed it.

Why is everyone a "spark expert"?? Get real and just show us how to do it...