I think both are very similar, but with slightly different goals. While they work transparently for each Hadoop application you need to enable specific support in the application for predicate push down. In the end you have to check which application you are using and do some tests (with correct predicate push down configuration). Keep in mind that both formats work best if they are sorted on filter columns (which is your responsibility) and if their optimatizations are correctly configured (min max index, bloom filter, compression etc) .
If you need to ingest sensor data you may want to store it first in hbase and then batch process it in large files in Orc or parquet format. > On 26 Jul 2016, at 04:09, janardhan shetty <janardhan...@gmail.com> wrote: > > Just wondering advantages and disadvantages to convert data into ORC or > Parquet. > > In the documentation of Spark there are numerous examples of Parquet format. > > Any strong reasons to chose Parquet over ORC file format ? > > Also : current data compression is bzip2 > > http://stackoverflow.com/questions/32373460/parquet-vs-orc-vs-orc-with-snappy > This seems like biased.
