Think of Parquet as a format well-suited to writing very large datasets to disk, whereas Arrow is a format most suited to efficient storage in memory. You might read Parquet files from disk, and then materialize them in memory in Arrow's format.
Both formats are designed around the idiosyncrasies of the target medium: Parquet is not designed to support efficient random access because disks aren't good at that, but Arrow has fast random access as a core design principle, to give just one example. Henry > On Feb 25, 2016, at 8:10 AM, Sourav Mazumder <sourav.mazumde...@gmail.com> > wrote: > > Hi All, > > New to this. And still trying to figure out where exactly Arrow fits in the > ecosystem of various Big Data technologies. > > In that respect first thing which came to my mind is how does Arrow compare > with parquet. > > In my understanding Parquet also supports a very efficient columnar format > (with support for nested structure). It is already embraced (supported) by > various technologies like Impala (origin), Spark, Drill etc. > > The only think I see missing in Parquet is support for SIMD based > vectorized operations. > > Am I right or am I missing many other differences between Arrow and parquet > ? > > Regards, > Sourav
