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The Secret Powers of Parquet Files: What Every Data Pro Should Know

Parquet files are a go-to choice for big data and analytics, thanks to their columnar storage, schema evolution, and impressive compression. But there’s more to Parquet than the basics. It also offers some under appreciated features that can simplify your workflows and boost efficiency.

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Datapreneur
November 23, 2024

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.