![]() ![]() Source file Data Preview correctly showed me 3 rows. Let's use this JSON data file as an example And when this vertical JSON structural set contains several similar sets (array) then ADF Mapping Data Flows Flatten does a really good job by transforming it into a table with several rows (records). I like the analogy of the Transpose function in Excel that helps to rotate your vertical set of data pairs ( name : value) into a table with the column names and values for corresponding objects. Part 2: Transforming JSON to CSV with the help of Flatten task in Azure Data Factory - Part 2 (Wrangling data flows) ![]() What this new task does it helps to transform/transpose/flatten your JSON structure into a denormalized flatten datasets that you can upload into a new or existing flat database table. ![]() (2020-Mar- 19) Recently, Microsoft introduced a new Flatten task to the existing set of powerful transformations available in the Azure Data Factory (ADF) Mapping Data Flows. ![]()
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