Readable layout
---
name: azure-kusto
description: "Query and analyze data in Azure Data Explorer (Kusto/ADX) using KQL for log analytics, telemetry, and time series analysis. WHEN: KQL queries, Kusto database queries, Azure Data Explorer, ADX clusters, log analytics, time series data, IoT telemetry, anomaly detection."
license: MIT
metadata:
author: Microsoft
version: "1.2.1"
---
# Azure Data Explorer (Kusto) Query & Analytics
Execute KQL queries and manage Azure Data Explorer resources for fast, scalable big data analytics on log, telemetry, and time series data.
## Skill Activation Triggers
**Use this skill immediately when the user asks to:**
- "Query my Kusto database for [data pattern]"
- "Show me events in the last hour from Azure Data Explorer"
- "Analyze logs in my ADX cluster"
- "Run a KQL query on [database]"
- "What tables are in my Kusto database?"
- "Show me the schema for [table]"
- "List my Azure Data Explorer clusters"
- "Aggregate telemetry data by [dimension]"
- "Create a time series chart from my logs"
**Key Indicators:**
- Mentions "Kusto", "Azure Data Explorer", "ADX", or "KQL"
- Log analytics or telemetry analysis requests
- Time series data exploration
- IoT data analysis queries
- SIEM or security analytics tasks
- Requests for data aggregation on large datasets
- Performance monitoring or APM queries
## Overview
This skill enables querying and managing Azure Data Explorer (Kusto), a fast and highly scalable data exploration service optimized for log and telemetry data. Azure Data Explorer provides sub-second query performance on billions of records using the Kusto Query Language (KQL).
Key capabilities:
- **Query Execution**: Run KQL queries against massive datasets
- **Schema Exploration**: Discover tables, columns, and data types
- **Resource Management**: List clusters and databases
- **Analytics**: Aggregations, time series, anomaly detection, machine learning
## Core Workflow
1. **Discover Resources**: List available clusters and databases in subscription
2. **Explore Schema**: Retrieve table structures to understand data model
3. **Query Data**: Execute KQL queries for analysis, filtering, aggregation
4. **Analyze Results**: Process query output for insights and reporting
## Query Patterns
### Pattern 1: Basic Data Retrieval
Fetch recent records from a table with simple filtering.
**Example KQL**:
```kql
Events
| where Timestamp > ago(1h)
| take 100
```
**Use for**: Quick data inspection, recent event retrieval
### Pattern 2: Aggregation Analysis
Summarize data by dimensions for insights and reporting.
**Example KQL**:
```kql
Events
| summarize count() by EventType, bin(Timestamp, 1h)
| order by count_ desc
```
**Use for**: Event counting, distribution analysis, top-N queries
### Pattern 3: Time Series Analytics
Analyze data over time windows for trends and patterns.
**Example KQL**:
```kql
Telemetry
| where Timestamp > ago(24h)
| summarize avg(ResponseTime), percentiles(ResponseTime, 50, 95, 99) by bin(Timestamp, 5m)
| render timechart
```
**Use for**: Performance monitoring, trend analysis, anomaly detection
### Pattern 4: Join and Correlation
Combine multiple tables for cross-dataset analysis.
**Example KQL**:
```kql
Events
| where EventType == "Error"
| join kind=inner (
Logs
| where Severity == "Critical"
) on CorrelationId
| project Timestamp, EventType, LogMessage, Severity
```
**Use for**: Root cause analysis, correlated event tracking
### Pattern 5: Schema Discovery
Explore table structure before querying.
**Tools**: `kusto_table_schema_get`
**Use for**: Understanding data model, query planning
## Key Data Fields
When executing queries, common field patterns:
- **Timestamp**: Time of event (datetime) - use `ago()`, `between()`, `bin()` for time filtering
- **EventType/Category**: Classification field for grouping
- **CorrelationId/SessionId**: For tracing related events
- **Severity/Level**: For filtering by importance
- **Dimensions**: Custom properties for grouping and filtering
## Result Format
Query results include:
- **Columns**: Field names and data types
- **Rows**: Data records matching query
- **Statistics**: Row count, execution time, resource utilization
- **Visualization**: Chart rendering hints (timechart, barchart, etc.)
## KQL Best Practices
**🟢 Performance Optimized:**
- Filter early: Use `where` before joins and aggregations
- Limit result size: Use `take` or `limit` to reduce data transfer
- Time filters: Always filter by time range for time series data
- Indexed columns: Filter on indexed columns first
**🔵 Query Patterns:**
- Use `summarize` for aggregations instead of `count()` alone
- Use `bin()` for time bucketing in time series
- Use `project` to select only needed columns
- Use `extend` to add calculated fields
**🟡 Common Functions:**
- `ago(timespan)`: Relative time (ago(1h), ago(7d))
- `between(start .. end)`: Range filtering
- `startswith()`, `contains()`, `matches regex`: String filtering
- `parse`, `extract`: Extract values from strings
- `percentiles()`, `avg()`, `sum()`, `max()`, `min()`: Aggregations
## Best Practices
- Always include time range filters to optimize query performance
- Use `take` or `limit` for exploratory queries to avoid large result sets
- Leverage `summarize` for aggregations instead of client-side processing
- Store frequently-used queries as functions in the database
- Use materialized views for repeated aggregations
- Monitor query performance and resource consumption
- Apply data retention policies to manage storage costs
- Use streaming ingestion for real-time analytics (< 1 second latency)
- Integrate with Azure Monitor for operational insights
## MCP Tools Used
| Tool | Purpose |
|------|---------|
| `kusto_cluster_list` | List all Azure Data Explorer clusters in a subscription |
| `kusto_database_list` | List all databases in a specific Kusto cluster |
| `kusto_query` | Execute KQL queries against a Kusto database |
| `kusto_table_schema_get` | Retrieve schema information for a specific table |
**Required Parameters**:
- `subscription`: Azure subscription ID or display name
- `cluster`: Kusto cluster name (e.g., "mycluster")
- `database`: Database name
- `query`: KQL query string (for query operations)
- `table`: Table name (for schema operations)
**Optional Parameters**:
- `resource-group`: Resource group name (for listing operations)
- `tenant`: Azure AD tenant ID
## Fallback Strategy: Azure CLI Commands
If Azure MCP Kusto tools fail, timeout, or are unavailable, use Azure CLI commands as fallback.
### CLI Command Reference
| Operation | Azure CLI Command |
|-----------|-------------------|
| List clusters | `az kusto cluster list --resource-group ` |
| List databases | `az kusto database list --cluster-name --resource-group ` |
| Show cluster | `az kusto cluster show --name --resource-group ` |
| Show database | `az kusto database show --cluster-name --database-name --resource-group ` |
### KQL Query via Azure CLI
For queries, use the Kusto REST API or direct cluster URL:
```bash
az rest --method post \
--url "https://..kusto.windows.net/v1/rest/query" \
--body "{ \"db\": \"\", \"csl\": \"\" }"
```
### When to Fallback
Switch to Azure CLI when:
- MCP tool returns timeout error (queries > 60 seconds)
- MCP tool returns "service unavailable" or connection errors
- Authentication failures with MCP tools
- Empty response when database is known to have data
## Common Issues
- **Access Denied**: Verify database permissions (Viewer role minimum for queries)
- **Query Timeout**: Optimize query with time filters, reduce result set, or increase timeout
- **Syntax Error**: Validate KQL syntax - common issues: missing pipes, incorrect operators
- **Empty Results**: Check time range filters (may be too restrictive), verify table name
- **Cluster Not Found**: Check cluster name format (exclude ".kusto.windows.net" suffix)
- **High CPU Usage**: Query too broad - add filters, reduce time range, limit aggregations
- **Ingestion Lag**: Streaming data may have 1-30 second delay depending on ingestion method
## Use Cases
- **Log Analytics**: Application logs, system logs, audit logs
- **IoT Analytics**: Sensor data, device telemetry, real-time monitoring
- **Security Analytics**: SIEM data, threat detection, security event correlation
- **APM**: Application performance metrics, user behavior, error tracking
- **Business Intelligence**: Clickstream analysis, user analytics, operational KPIs
