Deequ vs Soda Core

Developers should learn Deequ when working with big data pipelines where ensuring data quality is critical, such as in data lakes, ETL processes, or machine learning workflows meets developers should use soda core when building or maintaining data pipelines to ensure data reliability and prevent downstream errors in analytics or machine learning models. Here's our take.

Deequ

Nice Pick

Developers should learn Deequ when working with big data pipelines where ensuring data quality is critical, such as in data lakes, ETL processes, or machine learning workflows

Pros

• It is particularly useful for automating data validation in production environments, helping catch issues like missing values, schema violations, or statistical anomalies early, which reduces errors and improves reliability in data-driven applications
• Related to: apache-spark, data-quality

Cons

• Specific tradeoffs depend on your use case

Soda Core

Developers should use Soda Core when building or maintaining data pipelines to ensure data reliability and prevent downstream errors in analytics or machine learning models

Pros

• It is particularly valuable in ETL/ELT processes, data warehousing projects, and data migration scenarios where consistent data quality is critical for business decisions
• Related to: data-quality-testing, etl-pipelines

Cons

• Specific tradeoffs depend on your use case

The Verdict

These tools serve different purposes. Deequ is a library while Soda Core is a tool. We picked Deequ based on overall popularity, but your choice depends on what you're building.

The Bottom Line

Deequ wins

Based on overall popularity. Deequ is more widely used, but Soda Core excels in its own space.