Data Subject Request API Version 1 and 2
Data Subject Request API Version 3
Platform API Overview
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ComposeID
Warehouse Sync API v2 Migration
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Getting Started
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Upgrade to Version 7
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Upgrade to Version 2 of the SDK
Getting Started
Identity
Web
Alexa
Node SDK
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Java SDK
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
Step 1. Create an input
Step 2. Create an output
Step 3. Verify output
Introduction
Outbound Integrations
Firehose Java SDK
Inbound Integrations
Data Hosting Locations
Compose ID
Glossary
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
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Activity
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mParticle Users and Roles
Analytics Free Trial
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Usage metering for value-based pricing (VBP)
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Welcome Page Announcements
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Events
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UTM Guide
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Apply All for Filter Where Clauses
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Analyses Introduction
Getting Started
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For Clauses
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Save Your Segmentation Analysis
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Explore Users from Segmentation
Getting Started with Funnels
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Interpreting a Funnel Analysis
Group By
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Manage Analyses in Dashboards
Dashboards––Getting Started
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User Segments
IDSync Overview
Use Cases for IDSync
Components of IDSync
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Default IDSync Configuration
Profile Conversion Strategy
Profile Link Strategy
Profile Isolation Strategy
Best Match Strategy
Aliasing
Overview
Create and Manage Group Definitions
Introduction
Catalog
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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
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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
Backfilling is the process of importing previously blocked data from a Quarantine Output, optionally transforming it, and then uploading it back into mParticle. We provide instructions and helper scripts for you to backfill blocked data to mParticle’s Events API below. You cannot replay blocked data through the UI.
Read more about our Blocking feature here.
Replaying event attributes requires replaying of events
Replaying event attributes is not possible without replaying their associated events, which can lead to event duplication.
Avoid additional MTU charges
If the backfilled MPID and the original MPID do not match, the user will be counted twice and the number of unique MPIDs that determines your mParticle bill will be impacted.
Sooner is better than later
We advise replaying data no longer than 2 weeks from the date it was quarantined. Many downstream tools will not accept data over a certain age. The sooner you replay data, the better.
Batch and event timestamps
To send data to mParticle via our Events API, events are stored in a batch (see our Events API docs pages for additional detail). Both mParticle batches and events have a timestamp attached to them. To ensure that events are backfilled with the original timestamp, it’s essential to preserve the value stored in the timestamp_unixtime_ms
field that each event object contains (the timestamp attached at the batch level can be ignored).
Avoid batch deduplication
To avoid batches from being deduplicated in mParticle’s internal data pipeline, make sure to remove the batch_id
from the blocked batch before backfilling it to mParticle.
Backfilling data requires some coding skills
To fix and replay data, you need to know how to code.
Backfilling blocked data is non-trivial because you typically are interested in backfilling data to several downstream event integrations.
Based on your unique set of target event integrations, you should devise a strategy for your data backfill. The following questions will guide your backfill strategy:
Which integrations do I need to backfill?
Different integrations have different limitations when it comes to receiving historical data. Establish the limitation of a target integration by reading their developer docs or by sending a small amount of test data through an mParticle connection.
Do I need to backfill unplanned event attributes?
Event attributes cannot be replayed without their associated events. You’ll need a strategy (e.g. deleting previously sent yet incomplete events) to avoid event duplication if you want to replay blocked event attributes.
Which mParticle Input should I use to backfill my data?
The cleanest solution is typically to create a new Custom Feed for the purpose of your backfill. You can connect only the integrations that you want to backfill to that feed and then tear it down again once the backfill is complete.
However, some integrations are not available through the Custom Feed Input. In those cases, you will need to either (i) use the keys and secret of the original Input (e.g. Web) in our backfill script or (ii) send data directly to the integration’s API (after transforming it to match their data model).
Once you have a strategy for your backfill, here are the steps to backfill your data:
context
node. Within the context node, you will see a node labeled block_metadata
. This node contains the data you have blocked. Reference our sample data below to understand the complete data structure. block_metadata
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