Web Quickstart
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Requirements
In addition to dbt being installed and a web events dataset being available in your database:
- Snowplow Javascript tracker version 2 or later implemented.
- Web Page context enabled (enabled by default in v3+).
- Page view events implemented.
- From version v0.13.0 onwards you must be using RDB Loader v4.0.0 and above, or BigQuery Loader v1.0.0 and above. If you are not using these versions, or are using the Postgres loader, you will need to set
snowplow__enable_load_tstamptofalsein yourdbt_project.ymland will not be able to use the consent models.
In addition to the standard privileges required by dbt, our packages by default write to additional schemas beyond just your profile schema. If your connected user does not have create schema privileges, you will need to ensure that the following schemas exist in your warehouse and the user can create tables in them:
<profile_schema>_derived<profile_schema>_scratch<profile_schema>_snowplow_manifest
Alternatively, you can override the output schemas our models write to, see the relevant package configuration page for how to do this.
Installation
Check dbt Hub for the latest installation instructions, or read the dbt docs for more information on installing packages. If you are using multiple packages you may need to up/downgrade a specific package to ensure compatibility.
Setup
1. Override the dispatch order in your project
To take advantage of the optimized upsert that the Snowplow packages offer you need to ensure that certain macros are called from snowplow_utils first before dbt-core. This can be achieved by adding the following to the top level of your dbt_project.yml file:
# dbt_project.yml
...
dispatch:
- macro_namespace: dbt
search_order: ['snowplow_utils', 'dbt']
If you do not do this the package will still work, but the incremental upserts will become more costly over time.
2. Adding the selector.yml file
Within the packages we have provided a suite of suggested selectors to run and test the models within the package together with the web model. This leverages dbt's selector flag. You can find out more about each selector in the YAML Selectors section.
These are defined in the selectors.yml file (source) within the package, however in order to use these selections you will need to copy this file into your own dbt project directory. This is a top-level file and therefore should sit alongside your dbt_project.yml file. If you are using multiple packages in your project you will need to combine the contents of these into a single file.
3. Check source data
This package will by default assume your Snowplow events data is contained in the atomic schema of your target.database. In order to change this, please add the following to your dbt_project.yml file:
# dbt_project.yml
...
vars:
snowplow_web:
snowplow__atomic_schema: schema_with_snowplow_events
snowplow__database: database_with_snowplow_events
Please note that your target.database is NULL if using Databricks. In Databricks, schemas and databases are used interchangeably and in the dbt implementation of Databricks therefore we always use the schema value, so adjust your snowplow__atomic_schema value if you need to.
4. Enabled desired contexts
The web package has the option to join in data from the following 3 Snowplow enrichments:
By default these are all disabled in the web package. Assuming you have the enrichments turned on in your Snowplow pipeline, to enable the contexts within the package please add the following to your dbt_project.yml file:
# dbt_project.yml
...
vars:
snowplow_web:
snowplow__enable_iab: true
snowplow__enable_ua: true
snowplow__enable_yauaa: true
5. Filter your data set
You can specify both start_date at which to start processing events and the app_id's to filter for. By default the start_date is set to 2020-01-01 and all app_id's are selected. To change this please add the following to your dbt_project.yml file:
# dbt_project.yml
...
vars:
snowplow_web:
snowplow__start_date: 'yyyy-mm-dd'
snowplow__app_id: ['my_app_1','my_app_2']
6. Verify page ping variables
The web package processes page ping events to calculate web page engagement times. If your tracker configuration for min_visit_length (default 5) and heartbeat (default 10) differs from the defaults provided in this package, you can override by adding to your dbt_project.yml:
# dbt_project.yml
...
vars:
snowplow_web:
snowplow__min_visit_length: 5 # Default value
snowplow__heartbeat: 10 # Default value
7. Additional vendor specific configuration
Verify which column your events table is partitioned on. It will likely be partitioned on collector_tstamp or derived_tstamp. If it is partitioned on collector_tstamp you should set snowplow__derived_tstamp_partitioned to false. This will ensure only the collector_tstamp column is used for partition pruning when querying the events table:
# dbt_project.yml
...
vars:
snowplow_mobile:
snowplow__derived_tstamp_partitioned: false
Add the following variable to your dbt project's dbt_project.yml file
# dbt_project.yml
...
vars:
snowplow_web:
snowplow__databricks_catalog: 'hive_metastore'
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'). This is needed to handle the database property within models/base/src_base.yml.
A more detailed explanation for how to set up your Databricks configuration properly can be found in Unity Catalog support.
8. Run your model
You can now run your models for the first time by running the below command (see the operation page for more information on operation of the package):
dbt run --selector snowplow_web