Azure Monitor Ingestion Java
Azure Monitor Ingestion Java is an code AI skill with a core value of |. It
helps developers solve real-world problems in the code domain, boosting
efficiency, automating repetitive tasks, and optimizing workflows.
|
Quick Facts
mkdir -p ./skills/azure-monitor-ingestion-java && curl -sfL https://raw.githubusercontent.com/sickn33/antigravity-awesome-skills/main/skills/azure-monitor-ingestion-java/SKILL.md -o ./skills/azure-monitor-ingestion-java/SKILL.mdRun in terminal / PowerShell. Requires curl (Unix) or PowerShell 5+ (Windows).
Skill Content
# Azure Monitor Ingestion SDK for Java
Client library for sending custom logs to Azure Monitor using the Logs Ingestion API via Data Collection Rules.
Installation
<dependency>
<groupId>com.azure</groupId>
<artifactId>azure-monitor-ingestion</artifactId>
<version>1.2.11</version>
</dependency>Or use Azure SDK BOM:
<dependencyManagement>
<dependencies>
<dependency>
<groupId>com.azure</groupId>
<artifactId>azure-sdk-bom</artifactId>
<version>{bom_version}</version>
<type>pom</type>
<scope>import</scope>
</dependency>
</dependencies>
</dependencyManagement>
<dependencies>
<dependency>
<groupId>com.azure</groupId>
<artifactId>azure-monitor-ingestion</artifactId>
</dependency>
</dependencies>Prerequisites
- Data Collection Endpoint (DCE)
- Data Collection Rule (DCR)
- Log Analytics workspace
- Target table (custom or built-in: CommonSecurityLog, SecurityEvents, Syslog, WindowsEvents)
Environment Variables
DATA_COLLECTION_ENDPOINT=https://<dce-name>.<region>.ingest.monitor.azure.com
DATA_COLLECTION_RULE_ID=dcr-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
STREAM_NAME=Custom-MyTable_CLClient Creation
Synchronous Client
import com.azure.identity.DefaultAzureCredential;
import com.azure.identity.DefaultAzureCredentialBuilder;
import com.azure.monitor.ingestion.LogsIngestionClient;
import com.azure.monitor.ingestion.LogsIngestionClientBuilder;
DefaultAzureCredential credential = new DefaultAzureCredentialBuilder().build();
LogsIngestionClient client = new LogsIngestionClientBuilder()
.endpoint("<data-collection-endpoint>")
.credential(credential)
.buildClient();Asynchronous Client
import com.azure.monitor.ingestion.LogsIngestionAsyncClient;
LogsIngestionAsyncClient asyncClient = new LogsIngestionClientBuilder()
.endpoint("<data-collection-endpoint>")
.credential(new DefaultAzureCredentialBuilder().build())
.buildAsyncClient();Key Concepts
| Concept | Description |
|---------|-------------|
| Data Collection Endpoint (DCE) | Ingestion endpoint URL for your region |
| Data Collection Rule (DCR) | Defines data transformation and routing to tables |
| Stream Name | Target stream in the DCR (e.g., `Custom-MyTable_CL`) |
| Log Analytics Workspace | Destination for ingested logs |
Core Operations
Upload Custom Logs
import java.util.List;
import java.util.ArrayList;
List<Object> logs = new ArrayList<>();
logs.add(new MyLogEntry("2024-01-15T10:30:00Z", "INFO", "Application started"));
logs.add(new MyLogEntry("2024-01-15T10:30:05Z", "DEBUG", "Processing request"));
client.upload("<data-collection-rule-id>", "<stream-name>", logs);
System.out.println("Logs uploaded successfully");Upload with Concurrency
For large log collections, enable concurrent uploads:
import com.azure.monitor.ingestion.models.LogsUploadOptions;
import com.azure.core.util.Context;
List<Object> logs = getLargeLogs(); // Large collection
LogsUploadOptions options = new LogsUploadOptions()
.setMaxConcurrency(3);
client.upload("<data-collection-rule-id>", "<stream-name>", logs, options, Context.NONE);Upload with Error Handling
Handle partial upload failures gracefully:
LogsUploadOptions options = new LogsUploadOptions()
.setLogsUploadErrorConsumer(uploadError -> {
System.err.println("Upload error: " + uploadError.getResponseException().getMessage());
System.err.println("Failed logs count: " + uploadError.getFailedLogs().size());
// Option 1: Log and continue
// Option 2: Throw to abort remaining uploads
// throw uploadError.getResponseException();
});
client.upload("<data-collection-rule-id>", "<stream-name>", logs, options, Context.NONE);Async Upload with Reactor
import reactor.core.publisher.Mono;
List<O🎯 Best For
- Claude users
- Software engineers
- Development teams
- Tech leads
💡 Use Cases
- Code quality improvement
- Best practice enforcement
📖 How to Use This Skill
- 1
Install the Skill
Copy the install command from the Terminal tab and run it. The SKILL.md file downloads to your local skills directory.
- 2
Load into Your AI Assistant
Open Claude and reference the skill. Paste the SKILL.md content or use the system prompt tab.
- 3
Apply Azure Monitor Ingestion Java to Your Work
Open your project in the AI assistant and ask it to apply the skill. Start with a small module to verify the output quality.
- 4
Review and Refine
Review AI suggestions before committing. Run tests, check for regressions, and iterate on the skill output.
❓ Frequently Asked Questions
Is Azure Monitor Ingestion Java compatible with Cursor and VS Code?
Yes — this skill works with any AI coding assistant including Cursor, VS Code with Copilot, and JetBrains IDEs.
Do I need specific dependencies for Azure Monitor Ingestion Java?
Check the install command and Works With section. Most code skills only require the AI assistant and your codebase.
How do I install Azure Monitor Ingestion Java?
Copy the install command from the Terminal tab and run it. The skill downloads to ./skills/azure-monitor-ingestion-java/SKILL.md, ready to use.
Can I customize this skill for my team?
Absolutely. Edit the SKILL.md file to add team-specific instructions, examples, or workflows.
⚠️ Common Mistakes to Avoid
Skipping validation
Always test AI-generated code changes, even for simple refactors.
Missing dependency updates
Check if the skill requires updated dependencies or new packages.