What techniques can you use to structure unstructured data in a dimensional model?

I agree that extracting useful or relevant feature is important for extracting unstructured data. Unstructured data contains a lot of information all of which may not be of business' importance. Focusing on important information also ensures that the ETL team does not drown in a pool of 'curated' data which is not used at all.

Leverage natural language processing to draw out key phrases and sentiments, parse the text to categorize and tag information, and employ machine learning for automated classification. Integrate this processed data with existing structured datasets using extract, transform, load (ETL) techniques. Establish hierarchies and taxonomies to give data a dimensional structure, ensuring to consolidate any duplicates for clear, unique dimensions. This approach effectively transforms unstructured data into a format primed for analysis in a dimensional model.

I guess the above suggestion is pretty good using nlp techniques , we tried with very naive approach we scrapped all the unstructured details of the customers in google sheet for temporary basis using pandas and then we created a dimension around it for the details!! This is very basic approach in the initial days which I did but I guess going ahead the idea of maintaining metadata for unstructured data would be helpful !!

This is the last step in the ETL process. During the last process, the converted data is now loaded into the data warehouse to complete the process. Depending on the situation, the data is updated by loading it into the data warehouse on a regular basis or more infrequently but more frequently. The quantity and timing of loading are totally determined by the needs and differ from system to system.

Some unstructured data such as XML can use a parser tool to extract. The process of extracting meaningful information from unstructured data sources and converting it into an easily studied structured format is known as data parsing. A tool or software used to automate this procedure is called a data parser. Text files and social media posts are examples of unstructured data, which can be challenging to work with because of their disorganization. But this data can be changed into structured data - organized into a particular format that makes analysis simple. The experience I have with parsers is in XML format data.

Creating metadata out of unstructured data e.g if you have bunch of pdf files, extract information out of your pdf by using tools like ICR ( intelligent character recognition, form recognized in azure and textract in aws ) extract tables, headings , or some contexts out of data and use it to enhance your dimenshional modeling. Select the components that are possible to change over time, follow scd type 1 or type 2 or mixed of both.