Warehouse Connectors: Sync data from your data warehouse into Mixpanel

With Warehouse Connectors you can sync data from data warehouses like Snowflake, BigQuery, Databricks, and Redshift to Mixpanel. By unifying business data with product usage events, you can answer many more questions in Mixpanel:

• What percentage of our Enterprise revenue uses the features we shipped last year?
• Did our app redesign reduce support tickets?
• Which account demographics have the best retention?
• We spent $50,000 on a marketing campaign, did the users we acquire stick around a month later?

Mixpanel's Mirror sync mode keeps the data in Mixpanel fully in sync with any changes that occur in the warehouse including updating historical events that are deleted or modified in your warehouse.

In this guide, we'll walk through how to set up Warehouse Connectors. The integration is completely codeless, but you will need someone with access to your DWH to help with the initial set up.

Getting Started

Step 1: Connect a warehouse

Navigate to Project Settings → Warehouse Sources (opens in a new tab). Select your warehouse and follow the instructions to connect it. Note: you only need to do this once.

  CREATE SCHEMA `<gcp-project>`.`mixpanel`
    OPTIONS (
      description = 'Mixpanel connector staging dataset',
      location = '<same-as-the-tables-to-be-synced>',
    );

Step 2: Load a warehouse table

Navigate to Project Settings → Warehouse Data (opens in a new tab) and click +Event Table.

Select a table or view representing an event from your warehouse and tell Mixpanel about the table. Once satisfied with the preview, click run and we’ll establish the sync. The initial load may take a few minutes depending on the size of the table, we show you progress as it’s happening.

🎉 Congrats, you’ve loaded your first warehouse table into Mixpanel! From this point onward, the table will be kept in sync with Mixpanel. You can now use this event throughout Mixpanel’s interface.

Table Types

Mixpanel’s Data Model consists of 4 types: Events, User Profiles, Group Profiles, and Lookup Tables. Each have properties, which are arbitrary JSON. Warehouse Connectors lets you turn any table or view in your warehouse into one of these 4 types of tables, provided they match the required schema.

Events

An event is something that happens at a point in time. It’s akin to a “fact” in dimensional modeling or a log in a database. Events have properties, which describe the event. Learn more about Events here.

Here’s an example table that illustrates what can be loaded as events in Mixpanel. The most important fields are the timestamp (when) and the user id (who) — everything else is optional.

Here are more details about the schema we expect for events:

User Profiles

A User Profile is a table that describes your users. It’s akin to a “dimension” in dimensional modeling or a relational table in a database. Learn more about User Profiles here.

Here’s an example table that illustrates what can be loaded as user profiles in Mixpanel. The only important column is the User ID, which is the primary key of the table.

While Profiles typically only store the state of a user as of now, Profile History enables storing the state of a user over time.

Setup

When creating a User Profile sync, set the Table Type to “History Table”. We expect tables to be modeled as a SCD (Slowly Changing Dimensions) Type 2 table. You will need to supply a Start Time column in the sync configuration. Mixpanel will infer a row's end time if a new row with a more recent start time for the same user is detected.

Supported warehouses: Snowflake, Big Query, Databricks

Source table requirements:

• The source table for user/group history is expected to be modeled as an SCD (Slowly Changing Dimension) Type 2 table. This means that the table must maintain all the history over time that you want to use for analysis.
• History tables are supported only with mirror sync mode. Follow these docs (opens in a new tab) to setup your source table to be mirror-compatible.
• The table should have a Timestamp/Date type column signifying the time that the properties on the row become active. This column will need to be supplied as Start Time in the sync configuration.
• The following data types are NOT supported:
  • Lists (eg Snowflake’s ARRAY)
  • Objects (eg Snowflake’s OBJECT)

Group Profiles

A Group Profile is a table that describes an entity (most often an Account, if you’re a B2B company). They are functionally identical to User Profiles, just used for other non-User entities. Group Profiles are only available if you have the Group Analytics add-on. Learn more about Group Analytics here.

Here’s an example table that illustrates what can be loaded as group profiles in Mixpanel. The only important column is the Group Key, which is the primary key of the table.

Group Profile History value and setup is similar to the User Profile History section elaborated above

Lookup Tables

A Lookup Table is useful for enriching Mixpanel properties (e.g. content, skus, currencies) with additional metadata. Learn more about Lookup Tables here. Do note the limits of lookup tables indicated here.

Here’s an example table that illustrates what can be loaded as lookup table in Mixpanel. The only important column is the ID, which is the primary key of the table that is eventually mapped to a Mixpanel property

Sync Modes

Warehouse Connectors regularly check warehouse tables for changes to load into Mixpanel. The Sync Mode determines which changes Mixpanel will reflect.

• Mirror will keep Mixpanel perfectly in sync with the data in the warehouse. This includes syncing new data, modifying historical data, and deleting data that has been removed from the warehouse. Mirror is supported for Snowflake and BigQuery.
• Append will load new rows in the warehouse into Mixpanel, but will ignore modifications to existing rows or rows that have been deleted from the warehouse. We recommend using Mirror over Append for supported warehouses.
• Full will reload the entire table to Mixpanel each time it runs rather than tracking changes between runs. Full syncs are only supported for Lookup Tables, User Profiles, and Group Profiles.
• One-Time will load the data from your warehouse into Mixpanel once with no ability to send incremental changer later. This is only recommended where the warehouse is being used as a temporary copy of the data being moved to Mixpanel from some other source and the warehouse copy will not be updated later.

Mirror

Mirror syncs work by having the warehouse compute which rows have been inserted, modified, or deleted and sending this list of changes to Mixpanel. Change tracking is configured differently depending on the source warehouse. Mirror is supported for Snowflake, Databricks and BigQuery sources.

ALTER TABLE songs ADD COLUMN Tag STRING NULL;

ALTER TABLE <table> ADD COLUMN <column> STRING NULL;

Append

Append syncs require an Insert Time column in your table. Mixpanel remembers the maximum Insert Time it saw in the previous run of the sync and looks for only rows that have an Insert Time greater than that. This is useful and efficient for append-only tables (usually events) that have a column indicating when the data was appended.

Each time an Append sync runs, it will query the source table with a WHERE <insert_time_column> > <previous_max_insert_time> clause. This means that records added with an append time value before the <previous_max_insert_time> from the previous run can be missed (not imported) as they would be considered already ingested. The <insert_time_column> value should always reflect when the value was made available for Mixpanel to query and ingest.

Considerations when using Append with large BigQuery tables:

In an un-partitioned BigQuery table, the <insert_time_column> filtering results in a full scan of all data in the source table each time the sync runs. To minimize BigQuery costs we recommend partitioning the source table by the <insert_time_column> (opens in a new tab). Doing so will ensure that each incremental sync run only scans the most recent partitions (opens in a new tab).

To understand the potential savings consider a 100 GB source table with 100 days of data (approximately 1 GB of data per day):

• If this table is not partitioned and is synced daily the Append sync will scan the whole table (100 GB of data) each time it runs, or 3,000 GB of data per month.
• If this table is partitioned by day and is synced daily with an Append sync the Append sync only scan the current day and previous day's partitions (2 GB of data) each time it runs, or 60 GB of data per day, a 50x improvement over the un-partitioned table.

Full

Full syncs periodically make a snapshot of the source table and sync it entirely to Mixpanel. If a row has new properties in your warehouse, the corresponding profile in Mixpanel will be overridden with those new properties. This mode is available for all tables except events.

Sync Frequency

Mixpanel offers a variety of sync frequency options to cater to different data integration needs. These options allow you to choose how often your data is synchronized from your data warehouse to Mixpanel, ensuring your data is up-to-date and accurate.

Standard Sync Frequency Options

• Hourly: Data is synchronized every hour, providing near real-time updates to your Mixpanel project.
• Daily: Data synchronization occurs once a day, ideal for daily reporting and analytics.
• Weekly: Data is synchronized once a week, suitable for less frequent reporting needs.

Advanced Sync Frequency Option: Trigger via API

For more advanced synchronization needs, Mixpanel offers the ability to trigger syncs via API. This option generates a PUT URL that customers can use in their code to orchestrate Mixpanel sync jobs with other jobs, such as Fivetran pipelines or dbt jobs. By using this API trigger option, you can ensure 100% accuracy by aligning Mixpanel syncs with other critical data operations.

To use the API trigger option:

1. Select Advanced>Trigger Via API under Sync Frequency in the table sync creation UI.
2. After creating the sync, we will generate a PUT URL for you.
3. Integrate this URL into your existing workflows or scripts.
4. Authenticate the request with a Mixpanel Service Account. More information on setting up and using Mixpanel Service Accounts can be found here (opens in a new tab).
5. Trigger the sync job programmatically, ensuring it runs in coordination with other data processes.

This flexibility allows you to maintain precise control over when and how your data is updated in Mixpanel, ensuring your analytics are always based on the latest information.

Note: If your table sync is set up with Mirror mode, you will need to run a sync job at least every 2 weeks to ensure our snapshots do not get deleted. We rate limit the number of syncs via API to 5 per hour.

FAQ

What tables are valuable to load into Mixpanel?

Anything that is event-based (has a user_id and timestamp) and that you want to analyze in Mixpanel. Examples, by data source are:

• CRM: Opportunity Created, Opportunity Closed
• Support: Ticket Created, Ticket Closed
• Billing: Subscription Created, Subscription Upgraded, Subscription Canceled, Payment Made
• Application Database: Sign-up, Purchased Item, Invited Teammate

We also recommend loading your user and account tables, to enrich events with demographic attributes about the users and accounts who performed them.

How fast do syncs transfer data?

Syncs have a throughput of ~30K events+updates/second or ~100M events+updates/hour.

What is the best way to start bringing in event data?

We recommend starting with a subset of data in a materialized view to test the import process. This allows you to ensure that relevant columns are correctly formatted and the data appears as expected in Mixpanel. Once the data is imported, run a few reports to verify that you can accurately gain insight into your team’s KPIs with the way your data is formatted.

After validating your use case, navigate to the imported table and select "Delete Import" to hard delete the subset data. This step ensures that you can then import the entire table without worrying about duplicate data.

I already track data to Mixpanel via SDK or CDP, can I still use Warehouse Connectors?

Yes! You can send some events (eg: web and app data) directly via our SDKs and send other data (eg: user profiles from CRM or logs from your backend) from your warehouse and analyze them together in Mixpanel.

How do I filter for events coming to mixpanel via Warehouse Connector Sync in my reports?

We add couple of hidden properties $warehouse_import_id and $warehouse_type on every event ingested through warehouse connectors. You can add filters and breakdowns on that property in any Mixpanel report. You can find the Warehouse import id of a sync in Sync History tab shown as Mixpanel Import Id.

How do updates & deletes from Mirror syncs affect my event quota usage?

On an Events billing plan your event quota is consumed by:

• Monthly Event Volume: The number of events in all of your projects at the end of each month. Updating an existing event using Mirror will not affect your monthly event volume. Deleting an existing event using Mirror will decrease your monthly event volume by one.
• Mirror updates and deletes: Updates and deletes from mirror are counted separately from event volume. Each update or delete is counted as one event towards billing for the month they were triggered on, even if the record being updated is for a previous month. You can see a breakdown your event quota consumption by "event volume" vs "updates and deletes" on your organization billing page (opens in a new tab).

You can see how much of your quota is being consumed by each warehouse connector in the detailed data usage view (opens in a new tab) for your organization.

On an MTU billing plan updates and deletes from Mirror do not impact MTU calculations, however updates and deletes are counted as events for the purposes of the MTU guardrail computation.

What will be the cost impact of this on my DWH?

The DWH cost of using a warehouse connector will vary based on the source warehouse and sync type used. Our connectors use warehouse-specific change tracking to compute modified rows in the warehouse and send only changed data to Mixpanel.

There are 3 aspects of DWH cost: network egress, storage, and compute.

• Network Egress: All data is transferred using gzip compression. Assuming an egress rate of $0.08 per GB and 100 compressed bytes per event, this is a cost of less than $0.01 per million events. Mirror and Append syncs will only transfer new or modified rows each time they run. Full syncs will transfer all rows every time they run. We recommend using Full syncs only for small tables and running them less frequently.
• Storage: Append and Full syncs do not store any additional data in your warehouse, so there are no extra storage costs. Mirror tracks changes using warehouse-specific functionality that can affect warehouse storage costs:
  • Snowflake: Mirror uses Snowflake Streams (opens in a new tab). Snowflake Streams will retain historical data (opens in a new tab) until it is consumed from the stream. As long as the warehouse connector runs regularly data will be consumed regularly and only retained between runs.
  • BigQuery: Mirror uses table snapshots (opens in a new tab). Mirror keeps one snapshot per table to track the contents of the table from the last run. BigQuery table snapshots have no cost when they are first created as they share the underlying storage with the source table, however as the source table changes the cost of storing changes is attributed to the table snapshot (opens in a new tab). Each time the connector runs the current snapshot is replaced with a new snapshot of the latest state of the table. The storage cost is the amount of changes being tracked between the snapshot and source table between runs.
  • Databricks: Mirror uses Databricks Change Data Feed (opens in a new tab) and all the changes are retained in databricks for the delta.logRetentionDuration (opens in a new tab). Configure that window accordingly to keep storage costs low.
• Compute:
  • Mirror on Snowflake: Snowflake Streams (opens in a new tab) natively track changes, the compute cost of querying for these changes is normally proportional to the amount of changed data.
  • Mirror on BigQuery: Each time the connector runs it checksums all rows in the source table and compares them to a table snapshot (opens in a new tab) from the previous run. For large tables we highly recommend partitioning (opens in a new tab) the source table. When the source table is partitioned the connector will skip checksumming any partitions which have not been modified since the last run. For more details see the BigQuery-specific instructions in Mirror.
  • Mirror on Databricks: Databricks Change Data Feed (opens in a new tab) natively tracks changes to the tables or views, the compute cost of querying these changes is normally proportional to the amount of changed data. Mixpanel reccomends using a smaller compute cluster and setting Auto Terminate after 10 mins of idle time on the compute cluster.
  • Append: All Append syncs run a query filtered on insert_time_column > [last-run-time], the compute cost is the cost of this query. Partitioning or clustering based on insert_time_column will greatly improve the performance of this query.
  • Full: Full syncs are always a full table scan of the source table to export it.

How can I get help setting up a warehouse connector?

Reach out (opens in a new tab) to our team — we’re happy to walk you through the set up. If you bring a data engineer who has credentials to access your warehouse, it takes < 10 minutes to get up and running.