Transforming JSON data in Power BI dataflows entails the following steps:
Start with JSON Data Load: The first step involved either connecting to a JSON file or retrieving it from an API within Power BI dataflows. Under the' Add Data' button, a user selects JSON in the data source type. If JSON is retrieved from an API, the respective API link and relevant authorization credentials are provided.
Parsing or flattening the nested structure: Power BI understands nested JSON files or any other data source as multi-layered hierarchies. The Power Query Editor can be used to flatten these fields. Locate columns that contain internal data and click the 'Expand' button, which is a table with an arrow (a table-shaped icon). Do this for each level of the hierarchy until all JSON elements have been flattened completely.
Accommodate for Changes In Structures: JSON, by default, is the very definition of variance from itself. For this reason, please explain that even mandatory attributes can sometimes not have values; thus, there is a need to 'Add Conditional Column' in order to provide such. Also, one may apply Power Query's "Table.TransformColumnTypes" function to avert disparities caused by data types during every refresh.
Tilt: Power Query transformations are applied to datasets to clean and polish the data. This includes renaming columns, Filtering out certain rows, Pivoting, Unpivoting, or even Merging tables, where necessary. For instance, in the case where a single field in the json contains more than one value, one may use "Split Column by Delimiter".
Enhance Efficiency: Transformation should be kept to a minimum as far as refresh times are concerned. Nested transformations should not be more than required, and where possible, related steps should be integrated together.
Review and Load the Data: The transformed data should be previewed to check its correctness. If contented, conduct the dataflow in Power BI and attach the results to the data model. Enhance your service by using refresh scheduling.
