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E-commerce Quickstart

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Requirements​

In addition to dbt being installed and a web events dataset being available in your database:

  • A dataset of e-commerce web events from the Snowplow JavaScript tracker must be available in the database.
  • Have the webPage context enabled.
  • Have the following e-commerce contexts enabled: cart, checkout_step, page transaction, user
  • Track the e-commerce tracking action events on your website

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 e-commerce 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 selectors 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 the table labeled events. In order to change this, please add the following to your dbt_project.yml file:

# dbt_project.yml
...
vars:
  snowplow_ecommerce:
    snowplow__atomic_schema: schema_with_snowplow_events
    snowplow__database: database_with_snowplow_events
    snowplow__events_table: table_of_snowplow_events
Databricks only

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. Filter your data set​

You can specify both start_date at which to start processing events, the app_id's to filter for, and the event_name value to filter on. By default the start_date is set to 2020-01-01, all app_id's are selected, and only the snowplow_ecommerce_action name is being surfaced. To change this please add/modify the following in your dbt_project.yml file:

# dbt_project.yml
...
vars:
  snowplow_ecommerce:
    snowplow__start_date: 'yyyy-mm-dd'
    snowplow__app_id: ['my_app_1','my_app_2']
    snowplow__ecommerce_event_names: ['snowplow_ecommerce_action', 'my_custom_ecommerce_event']

5. Additional vendor specific configuration​

BigQuery Only

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_ecommerce:
    snowplow__derived_tstamp_partitioned: false

6. Removing unused modules​

The e-commerce package creates tables that depend on the existence of certain entities that are a part of the Snowplow e-commerce JS plugin. If, for some reason, you have not implemented them and would like to streamline your data modeling not to create empty tables, then you need to add that configuration to your dbt_project.yml file. Below you can see an example of what that would look like if you wanted to disable the cart entity

# dbt_project.yml

...
vars:
    snowplow_ecommerce:
        snowplow__disable_ecommerce_carts: true
...
models:
    snowplow_ecommerce:
        carts:
            +enabled: false

Adding these two configurations to your dbt_project.yml will ensure that the carts module is disabled.

Disabling the cart module using dbt run​

If you want to temporarily disable a module, or you just find it easier to use the command line, you can also do this in the command line when executing the dbt run command. You will need to run the following command to disable the carts module

dbt run --exclude carts --select snowplow_ecommerce --vars '{snowplow__disable_ecommerce_carts: true}'

7. 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_ecommerce
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