> For the complete documentation index, see [llms.txt](https://docs.datapancake.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.datapancake.com/guides/faq.md). # FAQ Frequently Asked Questions ## FAQ #### 1. How do we ensure data quality when loading files? If you use the **DataPancake XML Chunker** to load data, it reports a **hash value** you can check against a hash of the raw file. * A provided **stored procedure** can scan dynamic table columns for fields that are 100% null, which indicates potential issues. * DataPancake supports **custom null expressions** to make quality checks easier. * For deeper checks, you can: * Count XML elements in the raw file and compare them to rows in the final materialized tables. * Validate transformation expressions (e.g., formulas or UDFs) to avoid incorrect results. *** #### 2. Can we add metadata columns (e.g., filename, run date) to dynamic tables? Yes. You can add **virtual attributes** at the root or within any nested array. These can be populated using any valid SQL expression, including values from other source columns. *** #### 3. How does the automated XML file ingestion process work? We provide SQL code to automate the pipeline: * A task checks for new files in ADLS. * When a new file arrives, the **XML Chunker UDTF** brings the data into the **raw staging table** using a **variant column**. *** #### 4. Can we clean up dynamic tables with aliases, PKs, and FKs? Yes. You can alias: * Raw dynamic table names * Dynamic column names * Primary/foreign key column names * View names and view column names This allows you to simplify naming and hide unnecessary array data when creating views. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://docs.datapancake.com/guides/faq.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.