Export to data lakes

Satori provides built-in analytics, but if your studio has its own data infrastructure, you can export your event stream directly to your data warehouse.

To configure an integration, open Settings in the Satori console and select the Integrations tab. In the Data Lakes section, each platform has its own configuration tab with the required fields and setup instructions.

Satori supports five platforms:

PlatformNotes
BigQueryExports to Google BigQuery.
SnowflakeExports to Snowflake.
RedshiftExports to Amazon Redshift.
S3Exports to an Amazon S3 bucket.
DatabricksExports to Databricks via S3 in Parquet format. Ingest into Databricks following standard cloud object storage ingestion.

BigQuery #

The BigQuery tab enables you to configure the BigQuery adaptor for Satori, and displays the instructions for doing so.

For detailed information on BigQuery connection, see Connect to BigQuery.

BigQuery configuration panel showing GCP Project ID, BigQuery Dataset ID, Events Table Name, GCP service account credentials, and Schema Version fields

Snowflake #

The Snowflake tab enables you to configure the Snowflake adaptor for Satori, and displays the instructions for doing so.

For detailed information on Snowflake connection, see Connect to Snowflake.

Snowflake configuration panel showing Table Name and Snowflake URL fields

Redshift #

The Redshift tab enables you to configure the Redshift adaptor for Satori, and displays the instructions for doing so.

Redshift configuration panel showing Table Name and Redshift URL fields

S3 #

The S3 tab enables you to configure the S3 adaptor for Satori, and displays the instructions for doing so.

S3 configuration panel showing Access Key ID, Secret Access Key, Region, Bucket, Event Partitioning, Real-time, Flush Interval, and Max File Size fields

Databricks #

The Databricks (S3) tab enables you to configure data lake exports for Databricks Data Lakes via S3 adaptor. Using this integration, your data will be exported to your S3 bucket in Parquet File Format. You can ingest this data to your Databricks data lake following Databricks documentation.

Databricks (S3) configuration panel showing Access Key ID, Secret Access Key, Region, Bucket, Event Partitioning, Real-time, Flush Interval, and Max File Size fields