azure-kusto

Topics in the source

• Azure Data Explorer (Kusto) Query & Analytics
• Overview
• Core Workflow
• Query Patterns
• Pattern 1: Basic Data Retrieval
• Pattern 2: Aggregation Analysis
• Pattern 3: Time Series Analytics
• Pattern 4: Join and Correlation

Execute KQL queries and manage Azure Data Explorer resources for fast, scalable big data analytics on log, telemetry, and time series data. 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 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 Fetch recent reco

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