topic Re: What the best Framework/Package for data quality in Data Engineering

topic Re: What the best Framework/Package for data quality in Data Engineering https://community.databricks.com/t5/data-engineering/what-the-best-framework-package-for-data-quality/m-p/139397#M51189 <P>Here are few DQ packages for DLT or LDP that you can try.</P><P></P><P class=""><SPAN class="">1. Databricks Labs DQX</SPAN></P><UL><LI><SPAN class="">Purpose-built for Spark and Databricks.</SPAN></LI><LI><SPAN class="">Rule-based checks on </SPAN><SPAN class="">DataFrames</SPAN><SPAN class=""> (batch & streaming).</SPAN></LI><LI><SPAN class="">Supports quarantine and profiling.</SPAN></LI><LI><SPAN class="">Lightweight and easy to integrate.</SPAN></LI></UL><P class=""><SPAN class="">2. Great Expectations</SPAN></P><UL><LI><SPAN class="">Popular Python library for data validation.</SPAN></LI><LI><SPAN class="">Works with Spark, Pandas, SQL.</SPAN></LI><LI><SPAN class="">Rich set of expectations and auto-generated documentation.</SPAN></LI><LI><SPAN class="">Best for governance and transparency.</SPAN></LI></UL><P class=""><SPAN class="">3. </SPAN><SPAN class="">Cuallee</SPAN></P><UL><LI><SPAN class="">Lightweight, fast, and </SPAN><SPAN class="">DataFrame</SPAN><SPAN class="">-agnostic.</SPAN></LI><LI><SPAN class="">Supports </SPAN><SPAN class="">PySpark</SPAN><SPAN class="">, Pandas, Polars, </SPAN><SPAN class="">DuckDB</SPAN><SPAN class="">.</SPAN></LI><LI><SPAN class="">50+ built-in checks, minimal setup.</SPAN></LI></UL><P class=""><SPAN class="">4. Spark Expectations</SPAN></P><UL><LI><SPAN class="">Designed for Spark environments.</SPAN></LI><LI><SPAN class="">Uses decorators for defining rules.</SPAN></LI><LI><SPAN class="">Provides error tables and stats for monitoring.</SPAN></LI></UL><P class=""><SPAN class="">5. Pandas-DQ</SPAN></P><UL><LI><SPAN class="">For quick profiling and cleaning in Pandas.</SPAN></LI><LI><SPAN class="">HTML reports, duplicate/missing value checks.</SPAN></LI><LI><SPAN class="">Ideal for small datasets or pre-ingestion checks.</SPAN></LI></UL><P></P> Mon, 17 Nov 2025 16:09:45 GMT nayan_wylde 2025-11-17T16:09:45Z What the best Framework/Package for data quality https://community.databricks.com/t5/data-engineering/what-the-best-framework-package-for-data-quality/m-p/139385#M51183 <P>Hi everyone,</P><P>I’m currently looking for a data-quality solution for my environment. I don’t have DTL tables or a Unity Catalog in place.</P><P>In your opinion, what is the best framework or package to implement reliable data-quality checks under these conditions?</P><P>Thanks in advance!</P> Mon, 17 Nov 2025 15:41:22 GMT https://community.databricks.com/t5/data-engineering/what-the-best-framework-package-for-data-quality/m-p/139385#M51183 William_Scardua 2025-11-17T15:41:22Z Re: What the best Framework/Package for data quality https://community.databricks.com/t5/data-engineering/what-the-best-framework-package-for-data-quality/m-p/139397#M51189 <P>Here are few DQ packages for DLT or LDP that you can try.</P><P></P><P class=""><SPAN class="">1. Databricks Labs DQX</SPAN></P><UL><LI><SPAN class="">Purpose-built for Spark and Databricks.</SPAN></LI><LI><SPAN class="">Rule-based checks on </SPAN><SPAN class="">DataFrames</SPAN><SPAN class=""> (batch & streaming).</SPAN></LI><LI><SPAN class="">Supports quarantine and profiling.</SPAN></LI><LI><SPAN class="">Lightweight and easy to integrate.</SPAN></LI></UL><P class=""><SPAN class="">2. Great Expectations</SPAN></P><UL><LI><SPAN class="">Popular Python library for data validation.</SPAN></LI><LI><SPAN class="">Works with Spark, Pandas, SQL.</SPAN></LI><LI><SPAN class="">Rich set of expectations and auto-generated documentation.</SPAN></LI><LI><SPAN class="">Best for governance and transparency.</SPAN></LI></UL><P class=""><SPAN class="">3. </SPAN><SPAN class="">Cuallee</SPAN></P><UL><LI><SPAN class="">Lightweight, fast, and </SPAN><SPAN class="">DataFrame</SPAN><SPAN class="">-agnostic.</SPAN></LI><LI><SPAN class="">Supports </SPAN><SPAN class="">PySpark</SPAN><SPAN class="">, Pandas, Polars, </SPAN><SPAN class="">DuckDB</SPAN><SPAN class="">.</SPAN></LI><LI><SPAN class="">50+ built-in checks, minimal setup.</SPAN></LI></UL><P class=""><SPAN class="">4. Spark Expectations</SPAN></P><UL><LI><SPAN class="">Designed for Spark environments.</SPAN></LI><LI><SPAN class="">Uses decorators for defining rules.</SPAN></LI><LI><SPAN class="">Provides error tables and stats for monitoring.</SPAN></LI></UL><P class=""><SPAN class="">5. Pandas-DQ</SPAN></P><UL><LI><SPAN class="">For quick profiling and cleaning in Pandas.</SPAN></LI><LI><SPAN class="">HTML reports, duplicate/missing value checks.</SPAN></LI><LI><SPAN class="">Ideal for small datasets or pre-ingestion checks.</SPAN></LI></UL><P></P> Mon, 17 Nov 2025 16:09:45 GMT https://community.databricks.com/t5/data-engineering/what-the-best-framework-package-for-data-quality/m-p/139397#M51189 nayan_wylde 2025-11-17T16:09:45Z