Does this tool connect to my Azure account?

No. This tool runs entirely in your browser and generates configuration JSON that you can copy and paste into your infrastructure-as-code templates, CLI commands, or cloud console. It never connects to any cloud account or sends data externally.

Are the generated configurations production-ready?

The tool produces syntactically valid configurations based on current Azure service specifications. Always review generated configs against your organization security policies and test in a non-production environment before deploying.

Azure Data Explorer Query Builder

MonitoringAzure

Build ADX Kusto query configs with KQL queries for latency percentiles, anomaly detection, time series, and materialized views.

Azure Data Explorer (Kusto) Configuration

Build ADX Kusto query configs with KQL queries for latency percentiles, anomaly detection, time series analysis, and materialized views.

Required Fields

clusterNamedatabasequeriesqueries[0].namequeries[0].kql

{
  "clusterName": "adx-contoso-prod",
  "clusterUri": "https://adx-contoso-prod.eastus2.kusto.windows.net",
  "database": "TelemetryDB",
  "queries": [
    {
      "name": "RequestLatencyPercentiles",
      "description": "Calculate p50, p90, p95, p99 latencies by service over the last hour.",
      "kql": "HttpRequests\n| where Timestamp > ago(1h)\n| summarize\n    p50 = percentile(DurationMs, 50),\n    p90 = percentile(DurationMs, 90),\n    p95 = percentile(DurationMs, 95),\n    p99 = percentile(DurationMs, 99),\n    count()\n  by ServiceName, bin(Timestamp, 5m)\n| order by Timestamp desc, ServiceName asc",
      "resultFormat": "timechart"
    },
    {
      "name": "ErrorRateAnomaly",
      "description": "Detect anomalous error rate spikes using series decomposition.",
      "kql": "HttpRequests\n| where Timestamp > ago(24h)\n| summarize errorCount = countif(StatusCode >= 500), totalCount = count() by bin(Timestamp, 5m)\n| extend errorRate = todouble(errorCount) / totalCount * 100\n| make-series avgErrorRate = avg(errorRate) on Timestamp from ago(24h) to now() step 5m\n| extend (anomalies, score, baseline) = series_decompose_anomalies(avgErrorRate, 1.5)\n| mv-expand Timestamp to typeof(datetime), avgErrorRate to typeof(double), anomalies to typeof(int), score to typeof(double), baseline to typeof(double)\n| where anomalies != 0",
      "resultFormat": "table"
    },
    {
      "name": "TopSlowEndpoints",
      "description": "Find the slowest API endpoints by average response time.",
      "kql": "HttpRequests\n| where Timestamp > ago(1h)\n| where StatusCode < 500\n| summarize\n    avgDuration = avg(DurationMs),\n    maxDuration = max(DurationMs),\n    requestCount = count()\n  by RequestPath, Method\n| where requestCount > 10\n| top 20 by avgDuration desc",
      "resultFormat": "table"
    },
    {
      "name": "UserSessionFunnel",
      "description": "Analyze user session funnel from page view to purchase.",
      "kql": "let pageViews = AppEvents | where Name == 'PageView' | project SessionId, ViewTime = Timestamp;\nlet addToCart = AppEvents | where Name == 'AddToCart' | project SessionId, CartTime = Timestamp;\nlet purchases = AppEvents | where Name == 'Purchase' | project SessionId, PurchaseTime = Timestamp;\npageViews\n| join kind=leftouter addToCart on SessionId\n| join kind=leftouter purchases on SessionId\n| summarize\n    totalSessions = dcount(SessionId),\n    viewedProduct = dcountif(SessionId, isnotnull(ViewTime)),\n    addedToCart = dcountif(SessionId, isnotnull(CartTime)),\n    purchased = dcountif(SessionId, isnotnull(PurchaseTime))",
      "resultFormat": "table"
    }
  ],
  "tableSchema": {
    "HttpRequests": {
      "columns": [
        { "name": "Timestamp", "type": "datetime" },
        { "name": "ServiceName", "type": "string" },
        { "name": "RequestPath", "type": "string" },
        { "name": "Method", "type": "string" },
        { "name": "StatusCode", "type": "int" },
        { "name": "DurationMs", "type": "real" },
        { "name": "ClientIP", "type": "string" }
      ],
      "retentionDays": 365,
      "cacheDays": 31
    }
  },
  "materializedViews": [
    {
      "name": "HourlyErrorSummary",
      "sourceTable": "HttpRequests",
      "query": "HttpRequests | summarize errorCount = countif(StatusCode >= 500), totalCount = count() by ServiceName, bin(Timestamp, 1h)"
    }
  ]
}

Generated Output

Output will appear here...

About This Tool

Build ADX Kusto query configs with KQL queries for latency percentiles, anomaly detection, time series, and materialized views. This tool helps Azure engineers generate valid configurations quickly without consulting documentation, reducing errors and accelerating infrastructure deployment. All processing runs in your browser with no data sent to external servers.

When to Use This Tool

•Generating azure data explorer configurations for new infrastructure deployments.
•Validating existing configurations against best practices before applying changes.
•Learning Azure service configuration patterns through interactive examples.
•Accelerating infrastructure-as-code development by producing correct JSON/YAML starting points.

Frequently Asked Questions

Does this tool connect to my Azure account?: No. This tool runs entirely in your browser and generates configuration JSON that you can copy and paste into your infrastructure-as-code templates, CLI commands, or cloud console. It never connects to any cloud account or sends data externally.
Are the generated configurations production-ready?: The tool produces syntactically valid configurations based on current Azure service specifications. Always review generated configs against your organization security policies and test in a non-production environment before deploying.

Related Learning Guides

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Disclaimer: This tool runs entirely in your browser. No data is sent to our servers. Always verify outputs before using them in production. AWS, Azure, and GCP are trademarks of their respective owners.