Data Subject Request API Version 1 and 2
Data Subject Request API Version 3
Platform API Overview
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Warehouse Sync API Overview
Warehouse Sync API Tutorial
Warehouse Sync API Reference
Data Mapping
Warehouse Sync SQL Reference
Warehouse Sync Troubleshooting Guide
ComposeID
Warehouse Sync API v2 Migration
Bulk Profile Deletion API Reference
Calculated Attributes Seeding API
Data Planning API
Custom Access Roles API
Group Identity API Reference
Pixel Service
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Events API
mParticle JSON Schema Reference
IDSync
AMP SDK
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API Reference
Upgrade to Version 5
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Web
Android
iOS
Initialization
Configuration
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Kits
Application State and Session Management
Data Privacy Controls
Error Tracking
Opt Out
Push Notifications
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Preventing Blocked HTTP Traffic with CNAME
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Troubleshooting iOS SDK
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iOS 14 Guide
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API Reference
Upgrade to Version 7
Getting Started
Identity
Upload Frequency
Getting Started
Opt Out
Initialize the SDK
Event Tracking
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Error Tracking
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Location Tracking
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Initialization
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Event Tracking
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Opt Out
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Preventing Blocked HTTP Traffic with CNAME
Facebook Instant Articles
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API Reference
Upgrade to Version 2 of the SDK
Getting Started
Identity
Web
Alexa
Overview
Step 1. Create an input
Step 2. Verify your input
Step 3. Set up your output
Step 4. Create a connection
Step 5. Verify your connection
Step 6. Track events
Step 7. Track user data
Step 8. Create a data plan
Step 9. Test your local app
Overview
Step 1. Create an input
Step 2. Verify your input
Step 3. Set up your output
Step 4. Create a connection
Step 5. Verify your connection
Step 6. Track events
Step 7. Track user data
Step 8. Create a data plan
Overview
Step 1. Create an input
Step 2. Verify your input
Step 3. Set up your output
Step 4. Create a connection
Step 5. Verify your connection
Step 6. Track events
Step 7. Track user data
Step 8. Create a data plan
Step 1. Create an input
Step 2. Create an output
Step 3. Verify output
Node SDK
Go SDK
Python SDK
Ruby SDK
Java SDK
Introduction
Outbound Integrations
Firehose Java SDK
Inbound Integrations
Compose ID
Glossary
Data Hosting Locations
Migrate from Segment to mParticle
Migrate from Segment to Client-side mParticle
Migrate from Segment to Server-side mParticle
Segment-to-mParticle Migration Reference
Rules Developer Guide
API Credential Management
The Developer's Guided Journey to mParticle
Create an Input
Start capturing data
Connect an Event Output
Create an Audience
Connect an Audience Output
Transform and Enhance Your Data
The new mParticle Experience
The Overview Map
Introduction
Data Retention
Connections
Activity
Live Stream
Data Filter
Rules
Tiered Events
mParticle Users and Roles
Analytics Free Trial
Troubleshooting mParticle
Usage metering for value-based pricing (VBP)
Introduction
Sync and Activate Analytics User Segments in mParticle
User Segment Activation
Welcome Page Announcements
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Portfolio Analytics
Analytics Data Manager Overview
Events
Event Properties
User Properties
Revenue Mapping
Export Data
UTM Guide
Data Dictionary
Query Builder Overview
Modify Filters With And/Or Clauses
Query-time Sampling
Query Notes
Filter Where Clauses
Event vs. User Properties
Group By Clauses
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Apply All for Filter Where Clauses
Date Range and Time Settings Overview
Understanding the Screen View Event
Analyses Introduction
Getting Started
Visualization Options
For Clauses
Date Range and Time Settings
Calculator
Numerical Settings
Assisted Analysis
Properties Explorer
Frequency in Segmentation
Trends in Segmentation
Did [not] Perform Clauses
Cumulative vs. Non-Cumulative Analysis in Segmentation
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Save Your Segmentation Analysis
Export Results in Segmentation
Explore Users from Segmentation
Getting Started with Funnels
Group By Settings
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Interpreting a Funnel Analysis
Group By
Filters
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Analyze as Cohort from Funnel
Save a Funnel Analysis
Explore Users from a Funnel
Export Results from a Funnel
Saved Analyses
Manage Analyses in Dashboards
Dashboards––Getting Started
Manage Dashboards
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Time and Interval Settings in Dashboards
Query Notes in Dashboards
User Aliasing
The Demo Environment
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Analytics for Marketers
Analytics for Product Managers
Compare Conversion Across Acquisition Sources
Analyze Product Feature Usage
Identify Points of User Friction
Time-based Subscription Analysis
Dashboard Tips and Tricks
Understand Product Stickiness
Optimize User Flow with A/B Testing
User Segments
IDSync Overview
Use Cases for IDSync
Components of IDSync
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Identify Users
Default IDSync Configuration
Profile Conversion Strategy
Profile Link Strategy
Profile Isolation Strategy
Best Match Strategy
Aliasing
Overview
Create and Manage Group Definitions
Introduction
Catalog
Live Stream
Data Plans
Blocked Data Backfill Guide
Predictive Attributes Overview
Create Predictive Attributes
Assess and Troubleshoot Predictions
Use Predictive Attributes in Campaigns
Predictive Audiences Overview
Using Predictive Audiences
Introduction
Profiles
Warehouse Sync
Data Privacy Controls
Data Subject Requests
Default Service Limits
Feeds
Cross-Account Audience Sharing
Approved Sub-Processors
Import Data with CSV Files
CSV File Reference
Glossary
Video Index
Single Sign-On (SSO)
Setup Examples
Introduction
Introduction
Introduction
Rudderstack
Google Tag Manager
Segment
Advanced Data Warehouse Settings
AWS Kinesis (Snowplow)
AWS Redshift (Define Your Own Schema)
AWS S3 Integration (Define Your Own Schema)
AWS S3 (Snowplow Schema)
BigQuery (Snowplow Schema)
BigQuery Firebase Schema
BigQuery (Define Your Own Schema)
GCP BigQuery Export
Snowflake (Snowplow Schema)
Snowplow Schema Overview
Snowflake (Define Your Own Schema)
Aliasing
A SQL query can be provided as part of the Data Model request body with type set to sql. mParticle passes your SQL query to your warehouse,
so be sure to use valid syntax for your warehouse. For example, each warehouse has slightly different ways to utilize SQL functions with
different parameters.
The data in your warehouse is mapped to the user profile in mParticle according to the column names in the SQL you provide in the data model request following these rules:
"" or [] to preserve casing.The following SQL commands are fully supported:
The following SQL commands are not supported:
User attribute pipelines can be created without field transformations to allow for simpler mappings based on column names in your SQL query.
Every column is mapped to a user attribute except:
load_timestamp_field_type field (case-insensitive) in the data model requestFor a row to be associated to a profile, the following reserved column names are mapped to the mParticle User Identities or Device Identities. Additional columns are mapped as custom user attributes.
The following column names in your warehouse (case-insensitive) will be mapped to mParticle user identities automatically:
mpidcustomerid,customer_idfacebooktwittergooglemicrosoftyahooemailfacebook_custom_audience_idotherother_id_2other_id_3other_id_4other_id_5other_id_6other_id_7other_id_8other_id_9other_id_10mobile_numberphone_number_2phone_number_3The following column names in your warehouse (case-insensitive) will be mapped to mParticle device identities automatically:
android_uuidios_advertising_idpush_tokenios_idfvandroid_advertising_idamp_idroku_advertising_idroku_publisher_idmicrosoft_advertising_idmicrosoft_publisher_idfire_advertising_idmp_deviceidmParticle’s demo database has a few user attributes and alongside a prediction for the likelihood they may purchase. The following query will import this alongside their favorite categories as a simple list and apply it to the corresponding Profile according to their customer_id.
SELECT
a.date_updated,
a.customer_id,
a.firstname AS "$firstname",
c.propensity_to_buy AS "propensity_to_buy",
ARRAY_AGG(f.value) WITHIN GROUP (ORDER BY f.value ASC) AS "favorite_categories"
FROM mp.demo.attr a
JOIN mp.demo.calc c ON a.customer_id = c.customer_id
JOIN mp.demo.favs f ON a.customer_id = f.customer_id
GROUP BY
a.date_updated,
a.customer_id,
a.firstname,
c.propensity_to_buymParticle’s demo ticket database contains details about the number of requests a user has made. The following query will import the number of open tickets to that user’s Profile according to their e-mail address.
SELECT
u.email AS email,
COUNT(t.id) AS "count_of_open_tickets"
FROM mp.demo_service.tickets t
JOIN mp.demo_service.users u
ON u.id = t.requester_user_id
WHERE t.status = 'open'mParticle materializes your query by wrapping it in an outer SELECT statement to build more complex statements to execute against your data warehouse. These are the queries you will see looking at the history/audit logs in your data warehouse. For example, assume that a data model has the following query:
SELECT
a.scanned_timestamp_ms,
c.propensity_to_buy
FROM demodw.demo.mp_dw_demo_attr a
JOIN demodw.demo.mp_dw_demo_calc c ON a.customer_id = c.customer_idThe query will be wrapped into (but not limited to) queries such as:
Query the number of rows in your provided data model. The values in the filter predicate are available as data_interval_start and data_interval_end in the pipeline run status API:
SELECT COUNT(*)
FROM
(
SELECT a.scanned_timestamp_ms, c.propensity_to_buy
FROM demodw.demo.mp_dw_demo_attr a
JOIN demodw.demo.mp_dw_demo_calc c ON a.customer_id = c.customer_id
)
WHERE SCANNED_TIMESTAMP_MS BETWEEN '2023-03-01 14:28:55+0000' AND '2023-03-01 14:41:17+0000'Query the number of columns in your provided data model:
SELECT *
FROM
(
SELECT a.scanned_timestamp_ms, c.propensity_to_buy
FROM demodw.demo.mp_dw_demo_attr a
JOIN demodw.demo.mp_dw_demo_calc c ON a.customer_id = c.customer_id
)
LIMIT 0Query generated from a scheduled sync run. The values in the filter predicate are available as data_interval_start and data_interval_end in the pipeline run status API.
SELECT OBJECT_CONSTRUCT_KEEP_NULL(*)
FROM
(
SELECT a.scanned_timestamp_ms, c.propensity_to_buy
FROM demodw.demo.mp_dw_demo_attr a
JOIN demodw.demo.mp_dw_demo_calc c ON a.customer_id = c.customer_id
)
WHERE SCANNED_TIMESTAMP_MS BETWEEN '2023-03-01 14:28:55+0000' AND '2023-03-01 14:41:17+0000'Was this page helpful?