Parquet vs CSV

Developers should learn Parquet when working with big data analytics, as it significantly reduces storage costs and improves query performance by reading only relevant columns meets developers should learn and use csv when working with data export/import, data analysis, or interoperability between systems, as it is a universal format for sharing structured data. Here's our take.

Parquet

Nice Pick

Developers should learn Parquet when working with big data analytics, as it significantly reduces storage costs and improves query performance by reading only relevant columns

Pros

• It is essential for use cases involving data lakes, ETL pipelines, and analytical workloads where fast aggregation and filtering are required, such as in financial analysis, log processing, or machine learning data preparation
• Related to: apache-spark, apache-hive

Cons

• Specific tradeoffs depend on your use case

CSV

Developers should learn and use CSV when working with data export/import, data analysis, or interoperability between systems, as it is a universal format for sharing structured data

Pros

• It is particularly useful in scenarios like data migration, reporting, and integrating with tools like Excel, databases, or data processing libraries, where simplicity and broad compatibility are prioritized over complex features
• Related to: data-import-export, data-analysis

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.