Adjust
Adjust ETL connector for data replication
Features
Details
Release Status
In-dev
Source API Version
v1
Table Selection
Yes
Column Selection
Yes
Edit Integration
Yes (Tables, filters) / No (Columns, Fields)
Replication Type Selection
No
Authentication Parameters
API Key
Replication Type
Key Based Incremental
Replication Key
Date
Suggested Replication Frequency
2-4 hrs

Tables/APIs Supported:

--- | --- There are three types of tables (queries) that you can integrate: 1. Overview 2. Events 3. Cohorts
These largely mirror different sections in the Adjust dashboard, and allow you to see the relevant KPIs directly.
Data will be fetched in combinations of selected Attribution source, Attribution type, Reattributed and Period filter (in case of cohort queries).

Integrate Adjust with Daton

------ | ------ 1. Sign in to Daton 2. Select Adjust from Integrations page 3. Provide Integration Name, Replication Frequency, and History. Integration name would be used in creating tables for the integration and cannot be changed later 4. Provide API Key for authorizing Daton (https://help.adjust.com/en/article/kpi-service#authentication) and app token (https://help.adjust.com/en/article/app-settings#view-your-app-token) to extract data periodically 5. Post successful authentication, you will be prompted to choose from the list of available Adjust queries (tables) 6. Select from the list of kpis (columns) available for your app along with a combination of filters for each table and your UTC offset 7. Submit the integration 8. For integrating additional apps, one has to create a new integration from step-2.

Workflow

------ | ------ 1. Integrations would be in Pending state initially and will be moved to Active state as soon as the first job loads data successfully on to the configured warehouse 2. Users would be able to edit/pause/re-activate/delete integration anytime 3. Users can view job status and process logs from the integration details page by clicking on the integration name from the active list

Adjust Data

------ | ------
Each combination of filters would be used to query the trackers' data for that particular app as a table in the selected warehouse.
The data will be stored incrementally in a batch of 10 days in the warehouse as following base fields in their respective tables- 1. Networks (top level) 2. Campaigns (sublevel 1) 3. Adgroups (sublevel 2) 4. Creatives (sublevel 3)

Tables

Fields:
Name
Target Datatype
Name
STRING
Currency
STRING
StartDate
DATE
EndDate
DATE
Attribution source
STRING
Attribution type
STRING
Reattributed
STRING
utc_offset
STRING
Networks
RECORD
_daton_user_id
NUMERIC
_daton_batch_runtime
NUMERIC
_daton_batch_id
NUMERIC
Sub-fields:
  1. 1.
    Networks:
    Name
    Target Datatype
    Name
    STRING
    AppKpis
    RECORD
    EventKpis
    RECORD
    AdKpis
    RECORD
    CostKpis
    RECORD
    FraudKpis
    RECORD
    Campaigns
    RECORD
    1. 1.
      Campaigns:
      Name
      Target Datatype
      Name
      STRING
      AppKpis
      RECORD
      EventKpis
      RECORD
      AdKpis
      RECORD
      CostKpis
      RECORD
      FraudKpis
      RECORD
      AdGroups
      RECORD
      1. 1.
        AdGroups:
        Name
        Target Datatype
        Name
        STRING
        AppKpis
        RECORD
        EventKpis
        RECORD
        AdKpis
        RECORD
        CostKpis
        RECORD
        FraudKpis
        RECORD
        Creatives
        RECORD
        1. 1.
          Creatives
          Name
          Target Datatype
          Name
          STRING
          AppKpis
          RECORD
          EventKpis
          RECORD
          AdKpis
          RECORD
          CostKpis
          RECORD
          FraudKpis
          RECORD
          1. 1.
            AppKpis
      Name
      Target Datatype
      sessions
      FLOAT
      clicks
      FLOAT
      installs
      FLOAT
      uninstalls
      FLOAT
      deattributions
      FLOAT
      impressions
      FLOAT
      uninstall_cohort
      FLOAT
      reinstalls
      FLOAT
      click_conversion_rate
      FLOAT
      impression_conversion_rate
      FLOAT
      ctr
      FLOAT
      reattribution_reinstalls
      FLOAT
      reattributions
      FLOAT
      revenue_events
      FLOAT
      revenue
      FLOAT
      all_revenue
      FLOAT
      cohort_revenue
      FLOAT
      daus
      FLOAT
      waus
      FLOAT
      maus
      FLOAT
      limit_ad_tracking_installs
      FLOAT
      limit_ad_tracking_install_rate
      FLOAT
      limit_ad_tracking_reattributions
      FLOAT
      limit_ad_tracking_reattribution_rate
      FLOAT
      gdpr_forgets
      FLOAT
      cohort_ad_revenue
      FLOAT
      cohort_all_revenue
      FLOAT
    2. 2.
      EventKpis
      Name
      Target Datatype
      revenue
      RECORD
      events
      RECORD
      revenue_per_event
      RECORD
      revenue_events
      RECORD
      first_events
      RECORD
      revenue_per_revenue_event
      RECORD
      Each RECORD of EventKpis:
      Name
      Target Datatype
      EventName
      STRING
      Value
      FLOAT
    3. 3.
      AdKpis
      Name
      Target Datatype
      all_revenue
      FLOAT
      ad_revenue
      FLOAT
      ad_impressions
      FLOAT
      ad_rpm
      FLOAT
    4. 4.
      CostKpis
      Name
      Target Datatype
      install_cost
      FLOAT
      click_cost
      FLOAT
      impression_cost
      FLOAT
      cost
      FLOAT
      paid_installs
      FLOAT
      paid_clicks
      FLOAT
      paid_impressions
      FLOAT
      ecpc
      FLOAT
      ecpm
      FLOAT
      ecpi
      FLOAT
      cohort_gross_profit
      FLOAT
      return_on_investment
      FLOAT
      rcr
      FLOAT
      roas
      FLOAT
    5. 5.
      FraudKpis
      Name
      Target Datatype
      rejected_installs
      FLOAT
      rejected_installs_anon_ip
      FLOAT
      rejected_installs_too_many_engagements
      FLOAT
      rejected_installs_distribution_outlier
      FLOAT
      rejected_reattributions
      FLOAT
      rejected_reattributions_anon_ip
      FLOAT
      rejected_reattributions_too_many_engagements
      FLOAT
      rejected_reattributions_distribution_outlier
      FLOAT
      rejected_reattributions_click_injection
      FLOAT
      rejected_install_rate
      FLOAT
      rejected_install_anon_ip_rate
      FLOAT
      rejected_install_too_many_engagements_rate
      FLOAT
      rejected_install_distribution_outlier_rate
      FLOAT
      rejected_install_click_injection_rate
      FLOAT
      rejected_reattribution_rate
      FLOAT
      rejected_reattribution_anon_ip_rate
      FLOAT
      rejected_reattribution_too_many_engagements_rate
      FLOAT
      rejected_reattribution_distribution_outlier_rate
      FLOAT
      rejected_reattribution_click_injection_rate
      FLOAT
Fields:
Name
Target Datatype
Name
STRING
Currency
STRING
StartDate
DATE
EndDate
DATE
Attribution source
STRING
Attribution type
STRING
Reattributed
STRING
utc_offset
STRING
Networks
RECORD
_daton_user_id
NUMERIC
_daton_batch_runtime
NUMERIC
_daton_batch_id
NUMERIC
Sub-fields:
  1. 1.
    Networks:
    Name
    Target Datatype
    Name
    STRING
    Events
    RECORD
    Campaigns
    RECORD
    1. 1.
      Campaigns
      Name
      Target Datatype
      Name
      STRING
      Events
      RECORD
      AdGroups
      RECORD
      1. 1.
        AdGroups
        Name
        Target Datatype
        Name
        STRING
        Events
        RECORD
        Creatives
        RECORD
        1. 1.
          Creatives
          Name
          Target Datatype
          Name
          STRING
          Events
          RECORD
          Creatives
          RECORD
      2. 2.
        Events
        Name
        Target Datatype
        EventName
        STRING
        first_events
        FLOAT
        revenue_per_revenue_event
        FLOAT
        revenue_events
        FLOAT
        revenue
        FLOAT
        events
        FLOAT
        revenue_per_event
        FLOAT
Fields:
Name
Target Datatype
Name
STRING
Currency
STRING
StartDate
DATE
EndDate
DATE
Attribution source
STRING
Attribution type
STRING
Reattributed
STRING
utc_offset
STRING
period
STRING
Networks
RECORD
_daton_user_id
NUMERIC
_daton_batch_runtime
NUMERIC
_daton_batch_id
NUMERIC
Sub-fields:
  1. 1.
    Networks:
    Name
    Target Datatype
    Name
    STRING
    Periods
    RECORD
    Campaigns
    RECORD
    1. 1.
      Campaigns
      Name
      Target Datatype
      Name
      STRING
      Periods
      RECORD
      AdGroups
      RECORD
      1. 1.
        AdGroups
        Name
        Target Datatype
        Name
        STRING
        Periods
        RECORD
        Creatives
        RECORD
        1. 1.
          Creatives
          Name
          Target Datatype
          Name
          STRING
          Periods
          RECORD
          Creatives
          RECORD
    2. 2.
      Periods
      Name
      Target Datatype
      Period
      STRING
      time_spent_per_session
      FLOAT
      converted_user_size
      FLOAT
      conversion_per_user
      FLOAT
      conversion_distribution
      FLOAT
      converted_users
      FLOAT
      revenue_events_per_paying_user
      FLOAT
      events
      FLOAT
      conversion_per_active_user
      FLOAT
      revenue_events_per_active_user
      FLOAT
      revenue_events_per_user
      FLOAT
      first_uninstalls_total
      FLOAT
      first_reinstalls
      FLOAT
      first_uninstalls_total
      FLOAT
      reinstalls
      FLOAT
      reinstalls_total
      FLOAT
      gdpr_forgets
      FLOAT
      gdpr_forgets_total
      FLOAT
      first_uninstalls
      FLOAT
      uninstalls_total
      FLOAT
      uninstalls
      FLOAT
      reattributions_per_deattribution
      FLOAT
      deattributions_per_user
      FLOAT
      revenue_events
      FLOAT
      reattributions_per_user
      FLOAT
      deattributions
      FLOAT
      reattributions
      FLOAT
      events_per_active_user
      FLOAT
      events_per_user
      FLOAT
      events_per_converted_user
      FLOAT
      paying_users
      FLOAT
      paying_user_rate
      FLOAT
      paying_user_size
      FLOAT
      paying_users_retention_rate
      FLOAT
      cohort_size
      FLOAT
      retained_users
      FLOAT
      retention_rate
      FLOAT
      sessions
      FLOAT
      sessions_per_user
      FLOAT
      revenue
      FLOAT
      revenue_total
      FLOAT
      revenue_per_user
      FLOAT
      revenue_per_paying_user
      FLOAT
      revenue_total_in_cohort
      FLOAT
      revenue_events_total_in_cohort
      FLOAT
      lifetime_value
      FLOAT
      paying_user_lifetime_value
      FLOAT
      time_spent
      FLOAT
      time_spent_per_user
      FLOAT
Last modified 10mo ago