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Expected Segment data

Here’s a sample of the raw Segment data that Stitch will replicate to your analytics warehouse:


This table records all user information.

Table name: identify


This table records all event information.

Table name: track


This table records page events, along with any information about page properties.

Table name: page


This table records screen events, along with any information about screen properties.

Table name: screen


This table records group information, including traits and all additional properties.

Table name: group

View all tables

Learn more about the Segment integration


Detailed documentation on how to start syncing Segment data.

Segment Documentation

Jumpstart your Segment analytics with reusable blocks

dbt packages can speed up your work

Once you replicate your Segment data with Stitch, you can use it in many ways. For example, you can use the data modeling and transformation tool dbt to prepare data for reporting, analytics, or machine learning applications.

Dbt has prebuilt packages for many Stitch data sources, including Segment. Here’s a look at code for modeling Segment data. This particular block of code quickly cleans your Segment data and categorizes your web page views.

View the source on GitHub →
                          {% macro segment_web_page_views() %}
    {{ adapter_macro('segment.segment_web_page_views') }}
{% endmacro %}
{% macro default__segment_web_page_views() %}
with source as (
    select * from {{var('segment_page_views_table')}}
renamed as (
        id as page_view_id,
        received_at as received_at_tstamp,
        sent_at as sent_at_tstamp,
        timestamp as tstamp,
        url as page_url,
        {{ dbt_utils.get_url_host('url') }} as page_url_host,
        path as page_url_path,
        title as page_title,
        search as page_url_query,
        {{ dbt_utils.get_url_host('referrer') }} as referrer_host,
        context_campaign_source as utm_source,
        context_campaign_medium as utm_medium,
        context_campaign_name as utm_campaign,
        context_campaign_term as utm_term,
        context_campaign_content as utm_content,
        {{ dbt_utils.get_url_parameter('url', 'gclid') }} as gclid,
        context_ip as ip,
        context_user_agent as user_agent,
            when lower(context_user_agent) like '%android%' then 'Android'
            else replace(
                {{ dbt_utils.split_part(dbt_utils.split_part('context_user_agent', "'('", 2), "' '", 1) }},
                ';', '')
        end as device
        {% if var('segment_pass_through_columns') != [] %}
        {{ var('segment_pass_through_columns') | join (", ")}}
        {% endif %}
    from source
final as (
            when device = 'iPhone' then 'iPhone'
            when device = 'Android' then 'Android'
            when device in ('iPad', 'iPod') then 'Tablet'
            when device in ('Windows', 'Macintosh', 'X11') then 'Desktop'
            else 'Uncategorized'
        end as device_category
    from renamed
select * from final
{% endmacro %}
We set up all of the integrations we needed in a matter of hours. Writing custom code to do all of that would have taken up a month's worth of back-end engineering time. Using Stitch allowed us to move forward without having to hire another person.

Mark Marcantano

Senior DevOps Engineer, Calm

Why our customers choose Stitch

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