Yes, its compressed, read more for advanteges:
Parquet, is an open source file format for Hadoop, it stores nested data structures in a flat columnar format. Compared to a traditional approach where data is stored in row-oriented approach, parquet is more efficient in terms of storage and performance. Parquet stores binary data in a column-oriented way, where the values of each column are organized so that they are all adjacent, enabling better compression. It is especially good for queries which read particular columns from a “wide” (with many columns) table since only needed columns are read and IO is minimized.
When we are processing Big data, cost required to store such data is more (Hadoop stores data redundantly I.e 3 copies of each file to achieve fault tolerance) along with the storage cost processing the data comes with CPU,Network IO, etc costs. As the data increases cost for processing and storage increases. Parquet is the choice of Big data as it serves both needs, efficient and performance in both storage and processing.
To conclude main advantages of parquet:
- Organizing by column allows for better compression, as data is more homogeneous. The space savings are very noticeable at the scale of a Hadoop cluster.
- I/O will be reduced as we can efficiently scan only a subset of the columns while reading the data. Better compression also reduces the bandwidth required to read the input.
- As we store data of the same type in each column, we can use encoding better suited to the modern processors’ pipeline by making instruction branching more predictable.