Hi @stevesmith556,
Thank you for reaching out to Microsoft Fabric Community.
Here is the simple approach to deal with missing or corrupted data efficiently in Fabric Data Engineering
- Detect missing or corrupted data, use fabric’s Dataflow Gen2 or a Notebook to identify rows with missing (NULL or empty) or corrupted (invalid values) data.
- Handle missing data, remove unnecessary data, if a column or row contains too much missing data and isn’t useful, remove it.
- Fill missing values, replace missing values with appropriate defaults.
- Handle corrupted data, filter invalid rows and remove rows with corrupted or invalid formats using regular expressions, replace invalid data with logical defaults or transformations.
To effectively handle missing or corrupted data in fabric data engineering, can utilize Dataflow Gen2 and Notebooks. Here are the relevant microsoft documentation references to guide through the process:
Microsoft Fabric Dataflows:
Handle Missing and Corrupted Data in PySpark:
Data Wrangling and Power Query in Fabric:
By leveraging these official microsoft documents, we can implement to detect, handle, and prevent missing or corrupted data in data engineering workflows.
This approach will help efficiently handle missing or corrupted data in fabric's data engineering environment.
If this post helps, then please consider Accepting as solution to help the other members find it more quickly, don't forget to give a "Kudos" – I’d truly appreciate it!
Thank and regards,
Anjan Kumar Chippa
