Matillion ETL Shared Job

Transform and flatten data previously loaded by a Snapchat Extract.

Flatten Snapchat Extract

An Extract Nested Data component is used internally. This job is useful if you are iterating over multiple Snapchat tables, and the structure of the individual source tables may vary. In contrast, when using a plain Extract Nested Data component the structure of the source table must be known at design time.

Flattening is done to one level. Nested arrays or objects will become VARIANT columns in the flattened table.

The destination, flattened table will be recreated every time.

If there are no records in the extract table, the shared job will do nothing and finish successfully.


WarehouseSnowflake warehouse to use to process queries
Extract DatabaseDatabase containing the extracted data
Extract SchemaSchema containing the extracted data
Extract TableTable containing the extracted data
Flatten DatabaseDatabase to contain the flattened data
Flatten SchemaSchema to contain the flattened data
Flatten TableTable to contain the flattened data
Sample SizeNumber of rows to sample to find all columns and determine their data types
row_count (exported)(Exported value) Number of rows inserted into the Flatten Table. Only available if the extract contained data. Note: export the variable “row_count”, not “Row Count”


You must have previously run a Snapchat Extract component to colocate data from Snapchat to Snowflake. The table has one VARIANT column named Data Value.


Licensed under: Matillion Free Subscription License

Download METL-aws-sf-1.68.3-flatten-snapchat-extract.melt

Installation instructions

How to Install a Matillion ETL Shared Job

Author: Matillion