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.

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. If the target table's location is unknown and multiple candidate clusters, databases, or Log Analytics workspaces exist, batch the search (see Pattern 6) and only report "not found" after all candidates are checked.
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

Pattern 6: Batch Search Across Candidate Sources

When a table's location is unknown and there are several candidate clusters, databases, or workspaces, batch read-only metadata discovery instead of querying candidates one at a time. List tables in each candidate database or workspace and match the exact table name before querying data.

After confirming which sources contain the table, query those sources together when needed:

union workspace('ws1').Requests, workspace('ws2').Requests
| take 1

Use for: Locating a table among many candidate resources without confusing an empty result with a missing table

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

🔵 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

• Filter early with time range filters to optimize query performance and use indexed columns first
• 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