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.