🚀 Update Y2024: Just launched RudderStack Profiles
Scalable identity resolution in your warehouse for a 360 view of your customer

Identity Resolution using DBT and RudderStack

RudderStack supports different data warehouse destinations such as Redshift, BigQuery, and Snowflake. For each of these warehouses, certain predefined RudderStack tables get created, along with the tables for each type of event routed to RudderStack from different sources. This project leverages the identifies table that is created when any client application invokes the identify API of the RudderStack SDK. This API is typically invoked at the time of user login or registration.

This repository contains a sample DBT project for ID Resolution in a RudderStack-based Snowflake Data Warehouse.

Note: This project is based on the ID Resolution strategy and algorithms as described in our RudderStack blog on Identity Resolution

What is ID Resolution?

At a high level, ID Resolution is explained as follows:

• An end-user can navigate a site or application anonymously, i.e. without logging in. The user would then be associated or identified with an anonymous_id.
• At some point, the user might be required to register or login to perform some operation. A user_id would then get associated with that user.
• The user’s web or in-app activity might be scattered across multiple sessions, where sometimes he may be logged in, and sometimes not. They might also be accessing the site or the application from different devices.
• ID Resolution ties together all these different IDs to enable the enterprise or the developer to relate all the sessions and activities to a single user.

What Special DBT Features we use in this Project

We use the DBT incremental models for the following reasons:

• The ID linkages are built iteratively using two base tables. This process involves self-referencing
• The data from the identifies table is incrementally introduced into the mix to accommodate new IDs

We also use the DBT adapter and supported functions like check_relation.

How to Use this Repository

This project was created on the DBT Cloud. Hence, there is no profiles.yml file with the connection information. If you wish to execute the models in Command Line Interface (CLI) mode, you will need to create additional configuration files as documented here.

Sequence of Commands

The following is the sequence of commands to be run after setting up the project for the first time, or when a complete data rebuild is required:

• dbt run --full-refresh --models dbt_id_graph_base
• dbt run --full-refresh --models dbt_id_graph_prev
• dbt run --full-refresh --models dbt_id_graph_latest_curr
• dbt run --full-refresh --models dbt_id_graph_curr

Note: When doing a rebuild, all tables should be manually dropped first

The following commands may be run at regular intervals, in the sequence mentioned below:

• dbt run --models dbt_id_graph_curr
• dbt run --models dbt_id_graph_prev
• dbt run --models dbt_id_graph_latest_curr
• dbt run --full-refresh --models dbt_id_graph_curr

Some important points to note:

• At the end of each run cycle, the table <DATABASE>.<SCHEMA>.DBT_ID_GRAPH_CURR will have the latest ID linkages
• Some tests have been provided in this repository, which can be run to ensure that the tables being cycled are in a consistent state.
• Executing dbt test --data will execute the tests
• Remember to change DATABASE to your database name and SCHEMA to your schema name wherever applicable

More Information

For more information on ID Resolution or using the DBT macros, feel free to start a conversation on our Slack channel. We will be happy to help you.