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Data Manager API Event Ingestion

GoogleOfficialPublished by Google

  • Skill
  • Tool integration
  • Automation and integrations
  • Apache-2.0
  • No issues found

From the author

Guides developers through implementing event and conversion ingestion to Google products using the Data Manager API /v1/events/ingest endpoint and its associated client libraries. Use this skill when the user wants to upload offline conversions, enhanced conversions for leads, click conversions, Google Analytics web or app events, or any other event ingestion use case supported by the Data Manager API. Don't use for uploading audience members (use the data-manager-api-audience-ingestion skill).
— the description by Google

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a comprehensive guide for developers to integrate the Data Manager API for event and conversion ingestion. It covers the end-to-end implementation workflow, including client initialization, payload construction, data formatting, and diagnostic procedures. The skill emphasizes correct configuration of destination accounts, proper handling of user identifiers, and the use of official client libraries for Python, Java, PHP, Node, and .NET. It also includes critical migration paths for developers transitioning from legacy Google APIs.

What it does

  • Guides the implementation of event and conversion ingestion for Google Ads, Google Analytics, and Floodlight.
  • Provides standardized code samples for multiple programming languages including Python, Java, and Node.
  • Details the process for formatting, hashing, and encrypting user data using utility libraries.
  • Outlines the asynchronous diagnostic workflow for verifying ingestion status and handling errors.
  • Offers migration field mappings for users moving from Google Ads API or Measurement Protocol.
  • Highlights critical implementation gotchas regarding field naming, consent status, and identifier nesting.

When to use it

  • When you need to upload offline, click, or store sales conversions to Google Ads.
  • When you are implementing recommended or custom GA4 event tracking.
  • When you need to migrate existing conversion tracking from legacy Google APIs to the Data Manager API.
  • When you need to validate event schemas before performing a live data upload.
  • When you need to troubleshoot ingestion status or inspect field-level warnings.

Advantages

  • Ensures adherence to strict API formatting and normalization requirements.
  • Reduces integration errors by providing language-specific reference implementations.
  • Provides a clear path for asynchronous status monitoring and error diagnostics.
  • Simplifies the transition from older APIs with dedicated migration field mappings.

Usage examples

“How do I implement offline conversion ingestion for Google Ads using Python?”
The agent will provide the steps to initialize the IngestionServiceClient, format user data using the Formatter utility, and construct the IngestEventsRequest payload.
“I am migrating from the Google Analytics Measurement Protocol to Data Manager API. What fields do I need to map?”
The agent will provide the specific field mapping guide for GA4 events to ensure a smooth transition of your existing event tracking logic.
“My ingestion request returned a request_id, but how do I verify if the records were processed successfully?”
The agent will explain how to use the client.retrieve_request_status endpoint with the request_id to poll for processing results and inspect error or warning details.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/google/data-manager-api-event-ingestion/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip data-manager-api-event-ingestion.zip -d .agents/skills

Documentation by Google

Data Manager API Event Ingestion

Implementation Workflow

Prerequisites
  • Authentication & Library Installation: If you need to set up access to the Data Manager API or install the client and utility libraries, refer to the data-manager-api-setup skill.

Step 1: Identify Use Case & Read Documentation
  • Determine Destination Account Type: [CRITICAL] If it can't be determined from the user's context, consider clarifying which destination events are being ingested to before generating any code. This maps to the account_type field of the operating_account in the Destination, and also determines valid event identifiers and requirements.

  • Identify User Intent:

    • Implementing ingestion code: Follow the relevant implementation guide for the destination and use case in the Implementation guide column below. This is critical to ensure field requirements are met and destinations are correctly configured.

    • Checking request status or inspecting errors: Refer to the Error Handling & Troubleshooting section below.

    • Migrating from another Google API: Refer to Step 3: Retrieve Migration Guides below to extract the full contents of the relevant field mapping guide.

Destination (operating_account.account_type)Use caseImplementation guide
Google Ads (GOOGLE_ADS)Offline conversions, enhanced conversions for leadsSend events
Google Ads (GOOGLE_ADS)Multi-source conversions supplementing the Google tagSend events
Google Ads (GOOGLE_ADS)Store sales conversionsSend events
Google Analytics (GOOGLE_ANALYTICS_PROPERTY)Recommended and custom GA4 eventsSend events
Google Analytics (GOOGLE_ANALYTICS_PROPERTY)Multi-source events with a transaction IDSend events
Floodlight (FLOODLIGHT_CONFIG)Floodlight offline conversionsSend events
Floodlight (FLOODLIGHT_CONFIG)Multi-source conversions supplementing the Google or Floodlight tagSend events

If the request doesn't match any row, fetch the Events overview to find the right guide rather than guessing.

Step 2: Retrieve Code Sample

[!IMPORTANT] If writing or updating an ingestion script, ALWAYS retrieve the relevant code sample to use as a reference:

Step 3: Retrieve Migration Guides

[!IMPORTANT] If refactoring code to upgrade from another Google API, ALWAYS extract the full contents of the relevant field mapping guide.

Google Ads
Google Analytics
Floodlight
Step 4: Implementation

Implement the ingestion logic using the following checkpoints:

  • [ ] Initialize Client: Instantiate the Data Manager client (IngestionServiceClient).

  • [ ] Define Destinations: Build the Destination object using the product_destination_id and the appropriate account configurations: operating_account (target account receiving data), login_account (if authenticating using a manager account or a data partner account), and linked_account (if you're a data partner accessing the account via a partner link to a manager account). STRONGLY RECOMMENDED: Refer to the Configure destinations and headers guide for more details on configuring destinations.

  • [ ] Prepare Event Data: Use the utility library helpers to format and normalize user identifiers correctly.

  • [ ] Construct Payload: Build the request payload (IngestEventsRequest) containing the destinations, event records, and consent permissions.

  • [ ] Support Validation: Support sending the validate_only boolean option on the IngestEventsRequest to allow developers to validate schemas without actually uploading data.

  • [ ] Send Request: Execute ingest_events and record the returned request_id for later diagnostics.

  • [ ] Check for Ingestion Warnings: If any non-required field had a validation failure, the response from ingest_events will also include field_warnings, a list of FieldWarning objects detailing the issues.

  • [ ] Retrieve Request Status: Check the status of the ingestion request using diagnostics. Since request processing is asynchronous, a successful ingestion response (HTTP 200 OK returning a request_id) only indicates the payload was received. To check if the records actually succeeded, partially succeeded, or failed to process, query the client.retrieve_request_status endpoint using the request_id. Skipping this step is a common user mistake.

Formatting

  • Fetch the Format user data guide and use that as the source of truth for formatting and normalization rules.

  • Use the utility library to format, hash, and encrypt user data (emails, phone numbers, addresses).

    Python Example:

    from google.ads.datamanager_util import Formatter
    from google.ads.datamanager_util.format import Encoding
    
    formatter: Formatter = Formatter()
    
    processed_email: str = formatter.process_email_address(
        email, Encoding.HEX
    )

Critical Gotchas

  • Format product_destination_id as a numeric string. It is NOT a resource name path.

  • Format event_timestamp strictly in RFC 3339 format. Use the SDK's typed timestamp object instead of a raw string where available.

  • Nest click identifiers (gclid, gbraid, wbraid) inside the ad_identifiers block, not directly on the base event payload.

  • The enum values for ConsentStatus are CONSENT_GRANTED and CONSENT_DENIED. Do not use the values GRANTED and DENIED.

  • Note that consent can be set globally on the IngestEventsRequest or on individual Events.

  • Verify that UserIdentifier uses email_address and phone_number. Do not use the Google Ads API fields hashed_email and hashed_phone_number.

  • Ensure the currency field on the event is named currency, not currency_code.

  • Do not call the diagnostics endpoint (retrieve_request_status) if validate_only is set to true.

Error Handling & Troubleshooting

Inspecting Error Payloads & Ingestion Warnings

[!IMPORTANT] Refer to Understand API Errors for a detailed guide on how to understand the structure of errors and warnings returned by the API.

Retrieving Request Status (Diagnostics)

Periodically poll for status using exponential backoff, starting at least 30 minutes after sending the IngestEventsRequest.

  1. Call client.retrieve_request_status using RetrieveRequestStatusRequest(request_id=...).

  2. Loop through request_status_per_destination in the response to inspect each target's request_status.

  3. If processing is complete and request_status is SUCCESS, PARTIAL_SUCCESS, or FAILED, inspect diagnostic values:

    • Event Record Counts: Check events_ingestion_status.record_count (includes both success and failure).

    • Error Details: If status is FAILED or PARTIAL_SUCCESS, inspect each error's reason and record_count under error_info.error_counts.

    • Warning Details: Inspect each warning's reason and record_count under warning_info.warning_counts (even if the destination status is SUCCESS).

API Reference

Files

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs Python, Needs Node.js

Security

No issues found by our static scan· No issues found

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Attribution and licence

Published by
Google
Publisher
Google (Official)
Source
google/skills · skills/ads/data-manager-api-event-ingestion · commit 2194d3b
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
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Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download data-manager-api-event-ingestion.zip