azure-kusto

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

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:

Use for: Performance monitoring, trend analysis, anomaly detection

Pattern 4: Join and Correlation

Combine multiple tables for cross-dataset analysis.

Example KQL:

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

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

KQL Query via Azure CLI

For queries, use the Kusto REST API or direct cluster URL:

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