Apache Parquet vs CSV

Developers should learn Apache Parquet when working with big data analytics, as it significantly reduces storage costs and improves query performance by storing data in a columnar format, which is ideal for read-heavy workloads 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.

Apache Parquet

Nice Pick

Developers should learn Apache Parquet when working with big data analytics, as it significantly reduces storage costs and improves query performance by storing data in a columnar format, which is ideal for read-heavy workloads

Pros

• It is particularly useful in data engineering pipelines for ETL processes, data lake architectures, and scenarios requiring interoperability across multiple data processing frameworks, such as in cloud environments like AWS, Azure, or Google Cloud
• Related to: apache-spark, apache-hive

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

The Bottom Line

Apache Parquet wins

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