This dbt package:
- Provides macros to simplify the production of models that normalize Snowplow event data into a table per event type, plus a reduced events table and latest user context table, for easier integration with downstream tools.
- Includes a python script to generate the models using a simple configuration file that uses the schemas used within your pipelines to identify the columns, to greatly reduce the upfront effort in creating these models.
Please refer to the doc site for a full breakdown of the package.
The easiest way to get started is to follow our QuickStart guide.
The latest version of the snowplow-normalize package supports BigQuery, Databricks & Snowflake. For previous versions see our package docs.
- A dataset of Snowplow events must be available in the database.
Check dbt Hub for the latest installation instructions.
Please refer to the doc site for details on how to configure and run the package.
This package only contains the incremental models needed to efficiently process new events for your models, see the docs on incremental logic for more information. The package does however produce 3 types of models:
| Model | Description |
|---|---|
| event_name | A table for each of your specified event names, with flat columns from self-describing events and contexts. |
| filtered_events | A table that contains the event_ids, collector_tstamp, event name, and the name of the table that those events have been normalized into. Note it doesn't contain events not split out into individual tables. |
| event_users | A table with the latest user context columns for any user_ids in your events table. |
For more detailed information, see the doc site.
We welcome all ideas, questions and contributions!
For support requests, please use our community support Discourse forum.
If you find a bug, please report an issue on GitHub.
The snowplow-normalize package is Copyright 2022-2024 Snowplow Analytics Ltd.
This distribution is all licensed under the Snowplow Personal and Academic License . (If you are uncertain how it applies to your use case, check our answers to frequently asked questions.)