Mixpanel dbt Package

This dbt package transforms data from Fivetran's Mixpanel connector into analytics-ready tables.

Resources

• Number of materialized models¹: 6
• Connector documentation
  • Mixpanel connector documentation
  • Mixpanel ERD
• dbt package documentation
  • GitHub repository
  • dbt Docs
  • DAG
  • Changelog

What does this dbt package do?

This package enables you to better understand user activity and retention through your event data, create daily and monthly timelines of events with user metrics, and aggregate events into unique user sessions. It creates enriched models with metrics focused on user activity, retention, and event frequency.

Output schema

Final output tables are generated in the following target schema:

<your_database>.<connector/schema_name>_mixpanel

Final output tables

By default, this package materializes the following final tables:

Table	Description
mixpanel__event	Tracks de-duplicated user events with default Mixpanel properties and custom event-specific attributes to analyze individual user actions and behavior patterns across your product. Example Analytics Questions: Which events are most frequently performed by users across different platforms or browsers? How do custom event properties correlate with user retention or conversion outcomes? What event sequences lead to key conversion or engagement milestones?
mixpanel__daily_events	Aggregates daily event activity with user segmentation metrics including new, repeat, and returning users, plus trailing 7-day and 28-day active user counts to track engagement trends. Example Analytics Questions: How are daily active users (DAU) and weekly active users (WAU) trending by event type? What is the ratio of new users to repeat users performing key events each day? Which events show the strongest user retention based on returning and repeat user metrics?
mixpanel__monthly_events	Summarizes monthly event activity with cohort metrics including new, repeat, returning, and churned users, plus total monthly active users (MAU) to understand long-term engagement patterns. Example Analytics Questions: How are monthly active users (MAU) trending overall and by event type? What is the monthly user churn rate and how does it vary across different events? Which events have the highest proportion of new versus repeat users month-over-month?
mixpanel__sessions	Groups user events into sessions with metrics on event frequency and action types to analyze user engagement quality and session-level behavior patterns. Example Analytics Questions: What is the average event count per session and how frequently do users create sessions? Which sessions contain specific event types based on event frequency patterns? How do session metrics vary by device and user behavior patterns?

¹ Each Quickstart transformation job run materializes these models if all components of this data model are enabled. This count includes all staging, intermediate, and final models materialized as view, table, or incremental.

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Prerequisites

To use this dbt package, you must have the following:

• At least one Fivetran Mixpanel connection syncing data into your destination.
• A BigQuery, Snowflake, Redshift, PostgreSQL, or Databricks destination.

How do I use the dbt package?

You can either add this dbt package in the Fivetran dashboard or import it into your dbt project:

• To add the package in the Fivetran dashboard, follow our Quickstart guide.
• To add the package to your dbt project, follow the setup instructions in the dbt package's README file to use this package.

Install the package

Include the following mixpanel package version in your packages.yml file:

TIP: Check dbt Hub for the latest installation instructions or read the dbt docs for more information on installing packages.

packages:
  - package: fivetran/mixpanel
    version: [">=0.16.0", "<0.17.0"] # we recommend using ranges to capture non-breaking changes automatically

Databricks dispatch configuration

If you are using a Databricks destination with this package, you must add the following (or a variation of the following) dispatch configuration within your dbt_project.yml. This is required in order for the package to accurately search for macros within the dbt-labs/spark_utils then the dbt-labs/dbt_utils packages respectively.

dispatch:
  - macro_namespace: dbt_utils
    search_order: ['spark_utils', 'dbt_utils']

Database Incremental Strategies

Many of the models in this package are materialized incrementally, so we have configured our models to work with the different strategies available to each supported warehouse.

For BigQuery and Databricks All Purpose Cluster runtime destinations, we have chosen insert_overwrite as the default strategy, which benefits from the partitioning capability.

For Databricks SQL Warehouse destinations, models are materialized as tables without support for incremental runs.

For Snowflake, Redshift, and Postgres databases, we have chosen delete+insert as the default strategy.

Regardless of strategy, we recommend that users periodically run a --full-refresh to ensure a high level of data quality.

Define database and schema variables

Option A: Single connection

By default, this package runs using your destination and the mixpanel schema. If this is not where your Mixpanel data is (for example, if your Mixpanel schema is named mixpanel_fivetran), add the following configuration to your root dbt_project.yml file:

vars:
    mixpanel_database: your_database_name
    mixpanel_schema: your_schema_name 

Option B: Union multiple connections

If you have multiple Mixpanel connections in Fivetran and would like to use this package on all of them simultaneously, we have provided functionality to do so. For each source table, the package will union all of the data together and pass the unioned table into the transformations. The source_relation column in each model indicates the origin of each record.

To use this functionality, you will need to set the mixpanel_sources variable in your root dbt_project.yml file:

# dbt_project.yml

vars:
  mixpanel_sources:
    - database: connection_1_destination_name # Likely Required. Default value = target.database
      schema: connection_1_schema_name # Likely Required. Default value = 'mixpanel'
      name: connection_1_source_name # Required only if following the step in the following subsection

    - database: connection_2_destination_name
      schema: connection_2_schema_name
      name: connection_2_source_name

Note: If you choose to make use of this unioning functionality, you will incur an additional model materialized as a view, called stg_mixpanel__event_tmp. This extra model is necessary for the proper compilation of our connection-unioning macros.

Recommended: Incorporate unioned sources into DAG

If you are running the package through Fivetran Transformations for dbt Core™, the below step is necessary in order to synchronize model runs with your Mixpanel connections. Alternatively, you may choose to run the package through Fivetran Quickstart, which would create separate sets of models for each Mixpanel source rather than one set of unioned models.

Expand for details

By default, this package defines one single-connection source, called mixpanel, which will be disabled if you are unioning multiple connections. This means that your DAG will not include your Mixpanel sources, though the package will run successfully.

To properly incorporate all of your Mixpanel connections into your project's DAG:

1. Define each of your sources in a .yml file in your project. Utilize the following template for the source-level configurations, and, most importantly, copy and paste the table and column-level definitions from the package's src_mixpanel.yml file. This package currently only uses the EVENT source table.

# a .yml file in your root project
version: 2

sources:
  - name: <name> # ex: Should match name in mixpanel_sources
    schema: <schema_name>
    database: <database_name>
    loader: fivetran
    
    config:
      loaded_at_field: _fivetran_synced
      freshness: # feel free to adjust to your liking
        warn_after: {count: 72, period: hour}
        error_after: {count: 168, period: hour}

    tables:
      - name: event
        description: Table of all events tracked by Mixpanel across web, ios, and android platforms.
        columns: # copy and paste from mixpanel/models/staging/src_mixpanel.yml - see https://support.atlassian.com/bitbucket-cloud/docs/yaml-anchors/ for how to use &/* anchors to only do so once

2. Set the has_defined_sources variable (scoped to the mixpanel package) to True, like such:

# dbt_project.yml
vars:
  mixpanel:
    has_defined_sources: true

(Optional) Additional configurations

Collapse/expand details

Macros

analyze_funnel (source)

You can use the analyze_funnel(event_funnel, group_by_column, conversion_criteria) macro to produce a funnel between a given list of event types.

It returns the following:

• The number of events and users at each step
• The overall user and event conversion % between the top of the funnel and each step
• The relative user and event conversion % between subsequent steps

Note: The relative order of the steps is determined by their event volume, not the order in which they are input.

The macro takes the following as arguments:

• event_funnel: List of event types (not case sensitive).
  • Example: '['play_song', 'stop_song', 'exit']
• group_by_column: (Optional) A column by which you want to segment the funnel (this macro pulls data from the mixpanel__event model). The default value is None.
  • Example: group_by_column = 'country_code'.
• conversion_criteria: (Optional) A WHERE clause that will be applied when selecting from mixpanel__event.
  • Example: To limit all events in the funnel to the United States, you'd provide conversion_criteria = 'country_code = "US"'. To limit the events to only song play events to the US, you'd input conversion_criteria = 'country_code = "US"' OR event_type != 'play_song'.

Pivoting Out Event Properties

By default, this package selects the default columns collected by Mixpanel. However, you likely have custom properties or columns that you'd like to include in the mixpanel__event model.

If there are properties in the mixpanel.event.properties JSON blob that you'd like to pivot out into columns, add the following variable to your dbt_project.yml file:

vars:
  mixpanel:
    event_properties_to_pivot: ['the', 'list', 'of', 'property', 'fields'] # Note: this is case-SENSITIVE and must match the casing of the property as it appears in the JSON

Passthrough Columns

Additionally, this package includes all standard source EVENT columns defined in the staging_columns macro. You can add more columns using our passthrough column variables. These variables allow the passthrough fields to be aliased (alias) and casted (transform_sql) if desired, although it is not required. Data type casting is configured via a SQL snippet within the transform_sql key. You may add the desired SQL snippet while omitting the as field_name part of the casting statement - this will be dealt with by the alias attribute - and your custom passthrough fields will be casted accordingly.

Use the following format for declaring the respective passthrough variables:

vars:
  mixpanel:
    event_custom_columns:
      - name:           "property_field_id"
        alias:          "new_name_for_this_field_id"
        transform_sql:  "cast(property_field_id as int64)"
      - name:           "this_other_field"
        transform_sql:  "cast(this_other_field as string)"

Sessions Event Frequency Limit

The event_frequencies field within the mixpanel__sessions model reports all event types and the frequency of those events as a JSON blob via a string aggregation. For some users there can be thousands of different event types that take place. For Redshift and Postgres warehouses there currently exists a limit for string aggregations (up to 65,535). As a result, in order for Redshift and Postgres users to still leverage the event_frequencies field, an artificial limit is applied to this field of 1,000. If you would like to adjust this limit, you may do so by modifying the below variable in your project configuration.

vars:
  mixpanel:
    mixpanel__event_frequency_limit: 500 ## Default is 1000

Event Date Range

Because of the typical volume of event data, you may want to limit this package's models to work with a more recent date range.

By default, the package processes all events from your first recorded event. To override this and set a custom start date, add the following to your dbt_project.yml:

vars:
  mixpanel:
    date_range_start: 'yyyy-mm-dd' 

NOTE: This date range will not affect the number_of_new_users column in the mixpanel__daily_events or mixpanel__monthly_events models. This metric will be true new users.

Additionally, all final models are materialized as incremental. Updating the date_range_start in dbt_project.yml will only apply to newly ingested data. If you modify the date_range_start, we recommend running dbt run --full-refresh to ensure consistency across the adjusted date range.

Global Event Filters

In addition to limiting the date range, you may want to employ other filters to remove noise from your event data.

To apply a global filter to events (and therefore all models in this package), add the following variable to your dbt_project.yml file. It will be applied as a WHERE clause when selecting from the source table, mixpanel.event.

vars:
  mixpanel:
    # Ex: removing internal user
    global_event_filter: 'distinct_id != "1234abcd"'

Session Configurations

Session Inactivity Timeout

This package sessionizes events based on the periods of inactivity between a user's events on a device. By default, the package will denote a new session once the period between events surpasses 30 minutes.

To change this timeout value, add the following variable to your dbt_project.yml file:

vars:
  mixpanel:
    sessionization_inactivity: number_of_minutes # ex: 60

Session Pass-Through Columns

By default, the mixpanel__sessions model will contain the following columns from mixpanel__event:

• people_id: The ID of the user
• device_id: The ID of the device they used in this session
• event_frequencies: A JSON of the frequency of each event_type in the session

To pass through any additional columns from the events table to mixpanel__sessions, add the following variable to your dbt_project.yml file. The value of each field will be pulled from the first event of the session.

vars:
  mixpanel:
    session_passthrough_columns: ['the', 'list', 'of', 'column', 'names'] 

Session Event Criteria

In addition to any global event filters, you may want to disclude events or place filters on them in order to qualify for sessionization.

To apply any filters to the events in the sessions model, add the following variable to your dbt_project.yml file. It will be applied as a WHERE clause when selecting from mixpanel__event.

vars:
  mixpanel:

    # ex: limit sessions to include only these kinds of events
    session_event_criteria: 'event_type in ("play_song", "stop_song", "create_playlist")'

Lookback Window

Events can sometimes arrive late. For example, events triggered on a mobile device that is offline will be sent to Mixpanel once the device reconnects to wifi or a cell network. Since many of the models in this package are incremental, by default we look back 7 days to ensure late arrivals are captured while avoiding requiring a full refresh. To change the default lookback window, add the following variable to your dbt_project.yml file:

vars:
  mixpanel:
    lookback_window: number_of_days # default is 7

Changing the Build Schema

By default this package will build the Mixpanel staging models within a schema titled (<target_schema> + _stg_mixpanel) and Mixpanel final models within a schema titled (<target_schema> + mixpanel) in your target database. If this is not where you would like your modeled Mixpanel data to be written to, add the following configuration to your dbt_project.yml file:

models:
    mixpanel:
      +schema: my_new_schema_name # leave blank for just the target_schema
      staging:
        +schema: my_new_schema_name # leave blank for just the target_schema

Change the source table references (only if using a single connection)

If an individual source table has a different name than the package expects, add the table name as it appears in your destination to the respective variable. This is not available when running the package on multiple unioned connections.

IMPORTANT: See this project's dbt_project.yml variable declarations to see the expected names.

vars:
    mixpanel_<default_source_table_name>_identifier: your_table_name 

Event De-Duplication Logic

Events are considered duplicates and consolidated by the package if they contain the same:

• insert_id (used for de-deuplication internally by Mixpanel)
• people_id (originally named distinct_id)
• type of event
• calendar date of occurrence (event timestamps are set in the timezone the Mixpanel project is configured to)

This is performed in line with Mixpanel's internal de-duplication process, in which events are de-duped at the end of each day. This means that if an event was triggered during an offline session at 11:59 PM and resent when the user came online at 12:01 AM, these records would not be de-duplicated. This is the case in both Mixpanel and the Mixpanel dbt package.

(Optional) Orchestrate your models with Fivetran Transformations for dbt Core™

Expand for details

Fivetran offers the ability for you to orchestrate your dbt project through Fivetran Transformations for dbt Core™. Learn how to set up your project for orchestration through Fivetran in our Transformations for dbt Core setup guides.

Does this package have dependencies?

This dbt package is dependent on the following dbt packages. These dependencies are installed by default within this package. For more information on the following packages, refer to the dbt hub site.

IMPORTANT: If you have any of these dependent packages in your own packages.yml file, we highly recommend that you remove them from your root packages.yml to avoid package version conflicts.

packages:
    - package: fivetran/fivetran_utils
      version: [">=0.4.0", "<0.5.0"]

    - package: dbt-labs/dbt_utils
      version: [">=1.0.0", "<2.0.0"]

How is this package maintained and can I contribute?

Package Maintenance

The Fivetran team maintaining this package only maintains the latest version of the package. We highly recommend you stay consistent with the latest version of the package and refer to the CHANGELOG and release notes for more information on changes across versions.

Contributions

A small team of analytics engineers at Fivetran develops these dbt packages. However, the packages are made better by community contributions.

We highly encourage and welcome contributions to this package. Learn how to contribute to a package in dbt's Contributing to an external dbt package article.

Are there any resources available?

• If you have questions or want to reach out for help, see the GitHub Issue section to find the right avenue of support for you.
• If you would like to provide feedback to the dbt package team at Fivetran or would like to request a new dbt package, fill out our Feedback Form.