Normalize

Package Configuration Variables

This package utilizes a set of variables that are configured to recommended values for optimal performance of the models. Depending on your use case, you might want to override these values by adding to your dbt_project.yml file.

note

All variables in Snowplow packages start with snowplow__ but we have removed these in the below table for brevity.

Warehouse and tracker

Variable Name	Description	Default
atomic_schema	The schema (dataset for BigQuery) that contains your atomic events table.	atomic
database	The database that contains your atomic events table.	target.database
dev_target_name	The target name of your development environment as defined in your profiles.yml file. See the Manifest Tables section for more details.	dev
events	This is used internally by the packages to reference your events table based on other variable values and should not be changed.	events

Operation and logic

Variable Name	Description	Default
allow_refresh	Used as the default value to return from the allow_refresh() macro. This macro determines whether the manifest tables can be refreshed or not, depending on your environment. See the Manifest Tables section for more details.	false
backfill_limit_days	The maximum numbers of days of new data to be processed since the latest event processed. Please refer to the incremental logic section for more details.	30
days_late_allowed	The maximum allowed number of days between the event creation and it being sent to the collector. Exists to reduce lengthy table scans that can occur as a result of late arriving data. If set to -1 disables this filter entirely, which can be useful if you have events with no dvce_sent_tstamp value.	3
lookback_window_hours	The number of hours to look before the latest event processed - to account for late arriving data, which comes out of order.	6
start_date	The date to start processing events from in the package on first run or a full refresh, based on collector_tstamp.	'2020-01-01'
upsert_lookback_days	Number of days to look back over the incremental derived tables during the upsert. Where performance is not a concern, should be set to as long a value as possible. Having too short a period can result in duplicates. Please see the Snowplow Optimized Materialization section for more details.	30

Contexts, filters, and logs

Variable Name	Description	Default
app_id	A list of app_ids to filter the events table on for processing within the package.	[ ] (no filter applied)

Warehouse Specific

Databricks

Variable Name	Description	Default
databricks_catalog	The catalogue your atomic events table is in. Depending on the use case it should either be the catalog (for Unity Catalog users from databricks connector 1.1.1 onwards, defaulted to hive_metastore) or the same value as your snowplow__atomic_schema (unless changed it should be 'atomic').

Bigquery

Variable Name	Description	Default
derived_tstamp_partitioned	Boolean to enable filtering the events table on derived_tstamp in addition to collector_tstamp.	true

Snowflake

Variable Name	Description	Default
query_tag	This sets the value of the query_tag for all sql executed against the database. This is used internally for metric gathering in Snowflake and its value should not be changed.	snowplow_dbt

Output Schemas

By default all scratch/staging tables will be created in the <target.schema>_scratch schema, the derived tables, will be created in <target.schema>_derived and all manifest tables in <target.schema>_snowplow_manifest. Some of these schemas are only used by specific packages, ensure you add the correct configurations for each packages you are using. To change, please add the following to your dbt_project.yml file:

tip

If you want to use just your connection schema with no suffixes, set the +schema: values to null

# dbt_project.yml
...
models:
  snowplow_normalize:
    base:
      manifest:
        +schema: my_manifest_schema
      scratch:
        +schema: my_scratch_schema
        +tags: my_scratch_schema