# azure-monitor-ingestion-py

Guides Claude Code and GitHub Copilot in uploading custom logs to Azure Log Analytics via the Python Logs Ingestion API. Useful for telemetry ingestion scripts.

- skill · `type=skill` · Claude Code, GitHub Copilot · 2 installs · ★ 3149
- Author: Microsoft
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/microsoft/skills/tree/HEAD/.github/plugins/azure-sdk-python/skills/azure-monitor-ingestion-py/SKILL.md
- Updated: 2026-10-23
- HTML: https://affpapa.org/en/nevibecode/skills/azure-monitor-ingestion-py

## What it does

This skill guides coding assistants on using the `azure-monitor-ingestion` Python SDK. It provides implementation patterns for both sync and async `LogsIngestionClient` setups. You get ready structures for Data Collection Rules (DCR), custom log streams, and error callbacks. The underlying SDK splits records into chunks under 1MB and gzips them automatically.

You need active Azure prerequisites before sending data: a Log Analytics workspace, a Data Collection Endpoint, and a configured DCR. Authentication relies on `azure-identity` via `DefaultAzureCredential`. Without properly exported `AZURE_DCE_ENDPOINT` and `AZURE_DCR_RULE_ID` variables, data ingestion calls will fail immediately.

## Requirements
- Works with: Azure

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add microsoft/skills --skill azure-monitor-ingestion-py -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/azure-monitor-ingestion-py/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/azure-monitor-ingestion-py/

### Codex

**Manually: put the files in place**
- File: `.agents/skills/azure-monitor-ingestion-py/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

**Manually: put the files in place**
- File: `.cursor/skills/azure-monitor-ingestion-py/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install microsoft/skills azure-monitor-ingestion-py
```

2. **Manually: put the files in place**
- File: `.github/skills/azure-monitor-ingestion-py/SKILL.md`

### Gemini CLI

**Platform CLI**
```bash
gemini skills install https://github.com/microsoft/skills --path .github/plugins/azure-sdk-python/skills/azure-monitor-ingestion-py
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/azure-monitor-ingestion-py/SKILL.md` — The legacy .windsurf/skills/ path still works

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/microsoft/skills/tree/HEAD/.github/plugins/azure-sdk-python/skills/azure-monitor-ingestion-py/SKILL.md

## When to use it
- upload custom telemetry logs to Log Analytics via Logs Ingestion API
- configure async ingestion pipelines with `LogsIngestionClient`
- retry partially failed batches using an `on_error` callback
- stream log payloads from local JSON files to Azure Monitor

## Example prompts
- Write an async Python script to send application logs to Azure Monitor
- Show how to handle upload errors and retry failed records with LogsIngestionClient
- Generate code to upload events from logs.json using DefaultAzureCredential

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 1aa9c5c83cf91e7f.

BEGIN THIRD-PARTY FILE 1aa9c5c83cf91e7f
````text
---
name: azure-monitor-ingestion-py
description: |
  Azure Monitor Ingestion SDK for Python. Use for sending custom logs to Log Analytics workspace via Logs Ingestion API.
  Triggers: "azure-monitor-ingestion", "LogsIngestionClient", "custom logs", "DCR", "data collection rule", "Log Analytics".
license: MIT
metadata:
  author: Microsoft
  version: "1.0.0"
  package: azure-monitor-ingestion
---

# Azure Monitor Ingestion SDK for Python

Send custom logs to Azure Monitor Log Analytics workspace using the Logs Ingestion API.

## Installation

```bash
pip install azure-monitor-ingestion
pip install azure-identity
```

## Environment Variables

```bash
# Data Collection Endpoint (DCE)
AZURE_DCE_ENDPOINT=https://<dce-name>.<region>.ingest.monitor.azure.com  # Required for all auth methods

# Data Collection Rule (DCR) immutable ID
AZURE_DCR_RULE_ID=dcr-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx  # Required for all auth methods

# Stream name from DCR
AZURE_DCR_STREAM_NAME=Custom-MyTable_CL  # Required for all auth methods
AZURE_TOKEN_CREDENTIALS=prod # Required only if DefaultAzureCredential is used in production
```

## Prerequisites

Before using this SDK, you need:

1. **Log Analytics Workspace** — Target for your logs
2. **Data Collection Endpoint (DCE)** — Ingestion endpoint
3. **Data Collection Rule (DCR)** — Defines schema and destination
4. **Custom Table** — In Log Analytics (created via DCR or manually)

## Authentication & Lifecycle

> **🔑 Two rules apply to every code sample below:**
>
> 1. **Prefer `DefaultAzureCredential`.** It works locally (Azure CLI / VS Code / Developer CLI) and in Azure (managed identity, workload identity) with no code change. Avoid connection strings, account/API keys — they bypass Entra audit and rotation.
>    - Local dev: `DefaultAzureCredential` works as-is.
>    - Production: set `AZURE_TOKEN_CREDENTIALS=prod` (or `AZURE_TOKEN_CREDENTIALS=<specific_credential>`) to constrain the credential chain to production-safe credentials.
> 2. **Wrap every client in a context manager** so HTTP transports, sockets, and token caches are released deterministically:
>    - Sync: `with <Client>(...) as client:`
>    - Async: `async with <Client>(...) as client:` **and** `async with DefaultAzureCredential() as credential:` (from `azure.identity.aio`)
>
> Snippets may abbreviate this setup, but production code should always follow both rules.

```python
from azure.monitor.ingestion import LogsIngestionClient
from azure.identity import DefaultAzureCredential, ManagedIdentityCredential
import os

# Local dev: DefaultAzureCredential. Production: set AZURE_TOKEN_CREDENTIALS=prod or AZURE_TOKEN_CREDENTIALS=<specific_credential>
credential = DefaultAzureCredential(require_envvar=True)
# Or use a specific credential directly in production:
# See https://learn.microsoft.com/python/api/overview/azure/identity-readme?view=azure-python#credential-classes
# credential = ManagedIdentityCredential()

with LogsIngestionClient(
    endpoint=os.environ["AZURE_DCE_ENDPOINT"],
    credential=credential
) as client:
    # Use `client.upload(...)` for all subsequent operations (see examples below)
    ...
```

## Upload Custom Logs

```python
from azure.monitor.ingestion import LogsIngestionClient
````
END THIRD-PARTY FILE 1aa9c5c83cf91e7f

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