/azure-kusto

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**:

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**:

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**:

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**:

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` | Retriev