Output to Snowflake
Prerequisites
Ensure that you have a Snowflake connection with the correct permissions to write to your target table. Additionally, this target table should already exist within Snowflake before writing to it using Upsolver. Furthermore, you will need a storage connection that has access to the bucket you would like the job to use to store the intermediate files used while running the job. Finally, you should also have a staging table created previously that contains your ingested data.
Create a job writing to Snowflake
After you have fulfilled the prerequisites, you can create an INSERT
job as follows:
This example only uses a subset of all job options available when writing to Snowflake.
Depending on your use case, you may want to configure a different set of options. For instance, this example contains an aggregation, which means you may want to configure the AGGREGATION_PARALLELISM
option.
Alter a job writing to Snowflake
Certain job options are considered mutable, meaning that in some cases, you can run a SQL command to alter an existing transformation job rather than having to create a new one.
For example, take the job we created as an example earlier:
If you wanted to keep the job as is but just change the cluster that is running the job, you can run the following command:
Note that some options such as RUN_INTERVAL
cannot be altered once the connection has been created.
Drop a job writing to Snowflake
If you no longer need a certain job, you can easily drop it with the following SQL command:
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