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table.settings {

title:Profiles Sync Sample Queries

beta:true

plan:profiles

Through Profiles Sync, Segment provides data sets and models that can help you enrich customer profiles using any warehouse data available to you.

On this page, you’ll find queries that you can run with Profiles Sync to address common use cases.

title:Profiles Sync Tables and Materialized Views

beta:true

plan:profiles

Through Profiles Sync, Segment provides data sets and models that can help you enrich customer profiles using any warehouse data available to you.

Using the profile merge scenario, Segment would generate three new entries to this table:

<divstyle="overflow-x:auto;"markdown=1>

| ------------| ----------------------| -----------------------| ---------------------| -------------------|

|`profile_1`|`profile_1`|`page`|`event_1`| 2022-05-02 14:01:00|

|`profile_2`|`profile_2`|`page`|`event_3`| 2022-06-22 10:47:15|

|`profile_2`|`profile_1`|`identify`|`event_4`| 2022-06-22 10:48:00|

</div>

In this example, the table shows`profile_2` mapping to two places: first to itself, then, later, to`profile_1` after the merge occurs.

The anonymous site visits sample used earlier would generate the following events:

<divstyle="overflow-x:auto;"markdown=1>

| ------------| ----------------------| -----------------------| ---------------------| -------------------|

|`profile_1`|`profile_1`|`page`|`event_1`| 2022-05-02 14:01:00|

|`profile_2`|`profile_2`|`page`|`event_3`| 2022-06-22 10:47:15|

|`profile_2`|`profile_1`|`identify`|`event_4`| 2022-06-22 10:48:00|

(stacked vertically)

| -----------------------| -------------------| -------------------| -------------------|

|`timestamp`| 2022-05-02 14:01:00| 2022-06-22 10:47:15| 2022-06-22 10:48:00|

</div>

In this table, Segment shows three observed identifiers. For each of the three identifiers, Segment outputs the Segment ID initially associated with the identifier.

The previous result would generate two entries in the`pages` table:

<divstyle="overflow-x:auto;"markdown=1>

| ------------| ----------------------| --------------------| -----------------| ----------| -------------------|

|`profile_1`|`twilio.com`|`5285bc35-05ef-4d21`|`source_1`|`event_1`| 2022-05-02 14:01:00|

|`profile_2`|`twilio.com/education`|`b50e18a5-1b8d-451c`|`source_1`|`event_3`| 2022-06-22 10:47:15|

</div>

And two entries in the`identifies` table:

<divstyle="overflow-x:auto;"markdown=1>

| ------------| ----------------------------| --------------------| ----------------------| -----------------| ----------| -------------------|

|`profile_1`|`twilio.com/try_twilio`|`5285bc35-05ef-4d21`|`jane.kim@segment.com`|`source_1`|`event_2`| 2022-05-02 14:01:47|

|`profile_2`|`twilio.com/events/webinars`|`b50e18a5-1b8d-451c`|`jane.kim@segment.com`|`source_2`|`event_4`| 2022-06-22 10:48:00|

</div>

All these events were performed by the same person. If you use these tables to assemble your data models, though, always join them against`id_graph` to resolve each event’s`canonical_segment_id`.