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.

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 : Use for : Quick data inspection, recent event retrieval Pattern 2: Aggregation Analysis Summarize data by dimensions for insights and reporting. Example KQL : 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 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: 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 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 Retrieve schema information for a specific table 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 Operation Azure CLI Command List clusters az kusto cluster list resource group <rg name List databases az kusto database list cluster name <cluster resource group <rg name Show cluster az kusto cluster show name <cluster resource group <rg name Show database az kusto database show cluster name <cluster database name <db resource group <rg name 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 Table Location Unknown : Use Pattern 6 to batch search all candidate sources before concluding the data does not exist 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

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