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.
Parquet deploys Google's record-shredding and assembly algorithm that can address complex data structures within data storage. Some Parquet benefits include:
Fast queries that can fetch specific column values without reading full row data
Highly efficient column-wise compression
High compatibility with with OLAP
How is Parquet Different from CSV?
While CSV is simple and the most widely used data format (Excel, Google Sheets), there are several distinct advantages for Parquet, including:
Parquet is column oriented and CSV is row oriented. Row-oriented formats are optimized for OLTP workloads while column-oriented formats are better suited for analytical workloads.
Column-oriented databases such as AWS Redshift Spectrum bill by the amount data scanned per query
Therefore, converting CSV to Parquet with partitioning and compression lowers overall costs and improves performance
Parquet has helped its users reduce storage requirements by at least one-third on large datasets, in addition, it greatly improves scan and deserialization time, hence the overall costs.
Snowflake and Parquet
Snowflake reads Parquet data into a single Variant column (Variant is a tagged universal type that can hold up to 16 MB of any data type supported by Snowflake). Users can query the data in a Variant column using standard SQL, including joining it with structured data.
Snowflake now offers more nuanced control over data extraction from staged Parquet files. Users can selectively and efficiently project specific columns from Parquet files into distinct Snowflake table columns, streamlining the data transformation process and enabling more targeted data analysis.
See Snowflake’s capabilities for yourself. To give it a test drive, sign up for a free trial.