azure-kusto

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# 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**:
```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

### 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:
```kql
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

From microsoft/skills, MIT.

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