What is Parquet and what advantages it has over other file formats?

Apache Parquet is designed for efficient as well as performant flat columnar storage format of data compared to row based files like CSV or TSV files. Parquet uses the record shredding and assembly algorithm which is superior to simple flattening of nested namespaces.

What are the diff file formats in hive and what are the diff advantages of using Avro & parquet file formats?

AVRO is a row-based storage format, whereas PARQUET is a columnar-based storage format. PARQUET is much better for analytical querying, i.e., reads and querying are much more efficient than writing. Write operations in AVRO are better than in PARQUET. AVRO is much matured than PARQUET when it comes to schema evolution.

Why is Parquet faster?

Parquet is a columnar file format, so Pandas can grab the columns relevant for the query and can skip the other columns. This is a massive performance improvement. usecols can’t skip over entire columns because of the row nature of the CSV file format.

Why is Parquet format preferred?

Parquet is an open source file format for Hadoop. Parquet 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.

Is Parquet smaller than CSV?

Uncompressed CSV file: The uncompressed CSV file has a total size of 4 TB. Parquet file: If you compress your file and convert it to Apache Parquet, you end up with 1 TB of data in S3. However, because Parquet is columnar, Redshift Spectrum can read only the column that is relevant for the query being run.

What is Parquet storage?

Parquet is an open source file format built to handle flat columnar storage data formats. Parquet operates well with complex data in large volumes.It is known for its both performant data compression and its ability to handle a wide variety of encoding types.

Which is best file format for Hive?

ORC files
Using ORC files improves performance when Hive is reading, writing, and processing data comparing to Text,Sequence and Rc. RC and ORC shows better performance than Text and Sequence File formats.

Why are Parquet files better than CSV?

Parquet files are easier to work with because they are supported by so many different projects. Parquet stores the file schema in the file metadata. CSV files don’t store file metadata, so readers need to either be supplied with the schema or the schema needs to be inferred.

Is it better to have one large Parquet file or lots of smaller Parquet files?

The only downside of larger parquet files is it takes more memory to create them. So you can watch out if you need to bump up Spark executors’ memory. row groups are a way for Parquet files to have vertical partitioning.