Power BI does have certain restrictions when it comes to functionalities, especially handling unstructured data sources like logs or text data, which are non-tabular since Power BI carries an emphasis on importing structured or tabular data. This is where the data can be said to be brought to the appropriate level before being ingested into Power BI in the form of tables. Below are some action-oriented steps and other recommendations:
Pre-process Unstructured Data with External Tools: With regards to unstructured data, Power BI does not have many features that one can take advantage of, so it helps to try different features such as Python, R, or ETL. Python has some libraries, such as Pandas or PySpark, which help in extracting information from various other data sources, including logs, JSON files, and even plain text. When the data is organized in rows and columns, then it is possible to save it in any format, for instance, CSV and Excel, among others, which can then be uploaded to Power BI for further analysis.
Use of No-SQL Databases for Storage: Unstructured content that is too complicated can be stored in an unstructured or semi-structured database such as MongoDB or Cassandra. These databases are optimal for storing unstructured content. There are connectors for MongoDB and other NoSQL databases in Power BI Enables, which allow the user to manipulate and organize data in the Power Query interface of Power BI after arranging it beforehand in the database.
Employ Power Query for Refinement: Power Query in Power BI not only transforms ready-to-use data but also allows further modification of data that has been imported from unstructured databases or processed in some way. Functions such as Split Column, Extract Text, or Unpivot Columns may be employed to improve such data cleaning activities. In addition, when working with JSON or XML files, there are built-in features of Power Query that allow such data to be structured in a simpler, flat design.
With the introduction of external processing tools, the methodology itself, and the capabilities provided by Power BI with regard to processing the form of data that is seldom seen in tables and refined for analysis.
