Efficient Column Selection
Parquet’s columnar format allows selective reading of specific columns, significantly reducing memory usage and processing time.
Read parquet with selected column vs All columns
Accessing Row Group Metadata
Parquet files store metadata for each row group, including min/max values and null counts for each column. This hidden feature can help optimize queries by skipping irrelevant row groups
Read metadata
Output
Reading Parquet Files in Chunks
Large Parquet files can be processed in chunks to save memory, especially useful for files that don't fit entirely in memory. We can process each row group individually like below
Read Each Row Group
Output
Dynamic Schema Evolution
Unlike traditional file formats, Parquet supports schema evolution. For instance, you can add columns without rewriting old files.
Predicate Pushdown for Filtering Data
Predicate pushdown allows you to filter data at the storage layer itself, reducing the volume of data read into memory.
Filter Data while reading file
In summary, Whether you're optimizing analytics or building scalable pipelines, Parquet is a smart choice for modern data needs.