Transform and flatten data previously loaded by a 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.
Parameters
| Parameter | Description |
|---|---|
| Warehouse | Snowflake warehouse to use to process queries |
| Extract Database | Database containing the extracted data |
| Extract Schema | Schema containing the extracted data |
| Extract Table | Table containing the extracted data |
| Flatten Database | Database to contain the flattened data |
| Flatten Schema | Schema to contain the flattened data |
| Flatten Table | Table to contain the flattened data |
| Sample Size | Number 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” |
Prerequisites
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.
Downloads
Licensed under: Matillion Free Subscription License
Download METL-aws-sf-1.68.3-flatten-snapchat-extract.melt
- Target : Snowflake
- Version : 1.68.3 or higher
Installation instructions
How to Install a Matillion ETL Shared JobAuthor: Matillion