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azure-monitor-ingestion-py

Send custom logs to Log Analytics via the Logs Ingestion API.

Updated Apr 12, 2026
Search Tags:#python#log-analytics#logs-ingestion#dce#azure-monitor-ingestion#dcr
One-click install
npx skills add https://github.com/davidrrowley/CortexYouV3 --skill azure-monitor-ingestion-py-davidrrowley
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: azure-monitor-ingestion-py
Source: https://github.com/davidrrowley/CortexYouV3/tree/main/.agents/skills/azure-monitor-ingestion-py
Command: npx skills add https://github.com/davidrrowley/CortexYouV3 --skill azure-monitor-ingestion-py-davidrrowley

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Azure Monitor Ingestion SDK for Python enables sending custom logs to a Log Analytics workspace via the Logs Ingestion API. The SDK simplifies authentication, DCE/DCR configuration, and reliable log delivery for cloud-native applications.

Core Features & Use Cases

  • Supports sync and async clients to publish logs to a Log Analytics workspace using the Logs Ingestion API.
  • Handles authentication with DefaultAzureCredential and provides guidance for configuring DCE endpoints and DCR streams.
  • Demonstrates uploading logs that conform to a DCR schema, including error handling and basic batching for large payloads.
  • Use case: Ship application telemetry and diagnostic logs to a centralized Log Analytics workspace for monitoring and alerting.

Quick Start

Create a LogsIngestionClient and upload a sample set of logs to your configured DCR stream.

Frequently Asked Questions about azure-monitor-ingestion-py

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I send custom logs to a Log Analytics workspace using Python?▼

The Azure Monitor Ingestion SDK for Python sends custom logs to a Log Analytics workspace via the Logs Ingestion API. It simplifies authentication and reliable log delivery for cloud-native applications using sync and async clients.

What is a Data Collection Endpoint and Data Collection Rule for Azure Monitor logs?▼

A Data Collection Endpoint (DCE) provides the ingestion endpoint, while a Data Collection Rule (DCR) defines the stream and schema. The SDK integrates with both to route custom logs correctly into Azure Monitor.

Can I use DefaultAzureCredential to authenticate log ingestion to Azure Monitor?▼

Yes, the SDK handles authentication using DefaultAzureCredential. This allows you to securely connect to your DCE and DCR configurations without managing explicit connection strings manually.

Does the Azure Monitor Ingestion SDK support async clients for uploading logs?▼

Yes, the SDK supports both synchronous and asynchronous clients to publish logs. This allows you to support diverse ingestion scenarios and handle batched uploads with built-in error handling.

How do I handle large payloads when ingesting telemetry logs to Azure Monitor?▼

The SDK provides basic batching for large payloads during log ingestion. This ensures that logs conforming to a DCR schema are uploaded efficiently with integrated error handling for failed requests.

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