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As a result all of its historical issues and PRs have been closed.

Please do not clone this repo without understanding the risk in doing so:

• It may have unaddressed security vulnerabilities
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⛔🏚️ This package is no longer developed or maintained by dbt Labs. A fork is maintained at https://github.com/fleetio/dbt-segment

dbt-segment

This dbt package:

• Performs "user stitching" to tie all events associated with a cookie to the same user_id
• Transforms pageviews into sessions ("sessionization")

Installation instructions

New to dbt packages? Read more about them here.

1. Include this package in your packages.yml — check here for the latest version number.
2. Run dbt deps
3. Include the following in your dbt_project.yml directly within your vars: block (making sure to handle indenting appropriately). Update the value to point to your segment page views table.

# dbt_project.yml
config-version: 2
...

vars:
  segment:
    segment_page_views_table: "{{ source('segment', 'pages') }}"

This package assumes that your data is in a structure similar to the test file included in example_segment_pages. You may have to do some pre-processing in an upstream model to get it into this shape. Similarly, if you need to union multiple sources, de-duplicate records, or filter out bad records, do this in an upstream model.

4. Optionally configure extra parameters by adding them to your own dbt_project.yml file – see dbt_project.yml for more details:

# dbt_project.yml
config-version: 2

...

vars:
  segment:
    segment_page_views_table: "{{ source('segment', 'pages') }}"
    segment_sessionization_trailing_window: 3
    segment_inactivity_cutoff: 30 * 60
    segment_pass_through_columns: []
    segment_bigquery_partition_granularity: 'day' # BigQuery only: partition granularity for `partition_by` config

5. Execute dbt seed -- this project includes a CSV that must be seeded for it the package to run successfully.
6. Execute dbt run – the Segment models will get built as part of your run!

Database support

This package has been tested on Redshift, Snowflake, BigQuery, and Postgres.