Parquet vs CSV

Developers should learn and use Parquet when working with large-scale analytical data processing, as it significantly reduces storage costs and improves query performance through columnar compression and predicate pushdown meets developers should learn and use csv for handling lightweight data import/export tasks, such as migrating data between systems, generating reports, or processing datasets in analytics. Here's our take.

Parquet

Nice Pick

Developers should learn and use Parquet when working with large-scale analytical data processing, as it significantly reduces storage costs and improves query performance through columnar compression and predicate pushdown

Pros

• It is ideal for use cases such as data warehousing, log analysis, and machine learning pipelines where read-heavy operations dominate, and it integrates seamlessly with modern data ecosystems like cloud storage (e
• Related to: apache-spark, apache-hadoop

Cons

• Specific tradeoffs depend on your use case

CSV

Developers should learn and use CSV for handling lightweight data import/export tasks, such as migrating data between systems, generating reports, or processing datasets in analytics

Pros

• It is particularly useful in scenarios requiring interoperability with tools like Excel, data pipelines, or when working with structured data in a human-readable format without complex dependencies
• Related to: data-import, data-export

Cons

• Specific tradeoffs depend on your use case

The Verdict

These tools serve different purposes. Parquet is a database while CSV is a format. We picked Parquet based on overall popularity, but your choice depends on what you're building.

The Bottom Line

Parquet wins

Based on overall popularity. Parquet is more widely used, but CSV excels in its own space.