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Eventhouse CLI

MicrosoftOfficialPublished by Microsoft

  • Skill
  • Tool integration
  • Data and AI
  • MIT
  • Low risk
  • Has scripts

From the author

Authors and queries Fabric Eventhouse and KQL databases: tables, functions, policies, materialized views, ingestion, and read-only KQL for real-time and time-series analytics. All KQL database work belongs here, table creation and simple queries included. An Eventstream item is eventstream-cli; an Event Schema Set is eventschemaset-cli.
— the description by Microsoft

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The eventhouse-cli skill provides a structured interface for interacting with Microsoft Fabric Eventhouse and KQL databases. It acts as a mode-based dispatcher, separating administrative tasks like schema management and ingestion from read-only analytical queries. Users must select between authoring and consumption modes and consult the corresponding reference documentation before executing commands to ensure correct payload construction and operational safety.

What it does

  • Executes KQL management commands for table creation, alteration, and dropping.
  • Manages ingestion processes, including inline data, storage-based ingestion, and streaming policies.
  • Configures database policies such as retention, caching, and update policies.
  • Supports advanced operations like materialized views and stored functions.
  • Facilitates read-only KQL analytical queries and schema discovery.
  • Provides a framework for safe schema evolution and deployment scripts.

When to use it

  • When you need to define or modify the schema of a KQL database.
  • When performing real-time or time-series data analysis using KQL.
  • When automating data ingestion pipelines into Fabric Eventhouse.
  • When configuring database-level policies or materialized views.
  • When auditing database schema or monitoring ingestion health.

Advantages

  • Enforces a clear separation between read-only analytics and state-changing administrative tasks.
  • Provides standardized patterns for safe schema evolution and idempotent deployments.
  • Integrates directly with Fabric's REST API for programmatic database management.
  • Supports complex KQL operations including update policies and materialized views.

Usage examples

“Create a new KQL table named SensorData with columns for Timestamp, SensorId, and Value.”
The agent switches to authoring mode, reads the authoring reference, and executes a .create table command via the Fabric REST API.
“Show me the top 10 events from the last hour in the Telemetry database.”
The agent switches to consumption mode, reads the consumption reference, and executes a read-only KQL query against the specified database.
“Set a 30-day retention policy on the Events table.”
The agent switches to authoring mode, reads the authoring reference, and executes the .alter table policy retention command.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/microsoft/eventhouse-cli/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip eventhouse-cli.zip -d .agents/skills

Documentation by Microsoft

Telemetry — MANDATORY. Every api.fabric.microsoft.com call must carry x-ms-fabric-skill: eventhouse-cli (az rest: --headers "x-ms-fabric-skill=eventhouse-cli"), including every LRO poll, fabric_lro and retry. Snippets omit it — add it anyway.

CRITICAL NOTES

  1. To find the workspace details (including its ID) from workspace name: list all workspaces and, then, use JMESPath filtering

  2. To find the item details (including its ID) from workspace ID, item type, and item name: list all items of that type in that workspace and, then, use JMESPath filtering

  3. Skill disambiguation: use eventhouse-cli for anything expressed in KQL or stored in a KQL Database. Use the matching eventstream-cli mode for streaming topology, and the matching sqldw-* skill for T-SQL against a Warehouse or Lakehouse SQL endpoint.

Fabric Eventhouse / KQL Database -- CLI Skill

This one skill owns Fabric Eventhouse and KQL Database: schema and ingestion management plus read-only KQL analytics.

It is a mode dispatcher and contains NO procedures. Pick the mode that matches the request from the table below, then read the matching references/<mode>.md file end to end with your file-reading tool BEFORE issuing a single command. That file holds the endpoints, payload shapes, templates and gotchas; acting without it produces wrong payloads and wrong results.

Mode selection

ModeUse when the request ...Example triggersRead this first
authoringruns KQL management commands (.create, .alter, .ingest, .drop), policies, mappings, functions or materialized viewscreate kql table, kql ingestion, kql retention policy, kql function, materialized view, kql mappingreferences/authoring.md
consumptionruns read-only KQL (where, summarize, join, render), discovers schema with .show, or monitors ingestion healthkql query, query eventhouse, time-series kql, show tables kql, explore eventhousereferences/consumption.md
Mode boundary rule

consumption may only issue read-only KQL and .show commands. Any dot-command that changes state (.create, .alter, .ingest, .drop, .set-or-append) requires the authoring mode: say so, read references/authoring.md, then proceed.

If a request genuinely spans modes, handle them one at a time and read each reference before you start that part. If the mode is ambiguous after reading this table, ask one short clarifying question instead of guessing.

Reference index

Read the mode reference first; open a topic file below only when the task needs it. Every reference is listed here, so read it from this table rather than following a link out of another reference.

ReferenceRead it when
references/authoring.mdany authoring request -- start here
references/authoring-core.mdyou need the capability matrix, table/schema, ingestion, policy, external table or permission detail behind an authoring task
references/authoring-advanced-operations.mdmaterialized views, stored functions, update policies, schema evolution, or monitoring authoring operations
references/authoring-scripts.mdyou want a ready-to-run script for create-and-ingest, schema deployment, export or policies -- run them from scripts/, do not read them
references/consumption.mdany consumption request -- start here
references/consumption-discovery-queries.mdyou need the .show discovery queries for schema, tables or ingestion health
EVENTHOUSE-CONSUMPTION-CORE.mdconnection fundamentals, schema discovery and security, monitoring, performance best practices, or common KQL query patterns -- read before writing KQL

Terminal write -- the step you must not skip

Reading the reference and planning the change is NOT completing the task. Each mutating mode ends with one state-changing call. If you did not issue it, nothing was persisted -- say so explicitly rather than reporting success.

ModeTerminal write
authoringPOST .../v1/rest/mgmt carrying the literal .create-merge table (or CREATE TABLE-equivalent), .alter or .ingest command. Composing the KQL management command and showing it to the user is not executing it.
consumptionnone -- this mode is read-only

Before you report the task done, confirm the terminal call returned success and, where the reference documents a readback, read the artefact back to prove the change landed.

Shared essentials (all modes)

Resolve the workspace and item first; every mode depends on it.

TaskReferenceNotes
Finding Workspaces and Items in FabricCOMMON-CLI.mdMandatory -- read before resolving any workspace or item id
Fabric Topology & Key ConceptsCOMMON-CORE.mdItem types, workspaces, capacities
Environment URLsCOMMON-CORE.mdSovereign / non-public cloud hosts
Authentication & Token AcquisitionCOMMON-CORE.mdWrong audience = 401; read before any auth issue
Authentication RecipesCOMMON-CLI.mdaz login flows and token acquisition
Core Control-Plane REST APIsCOMMON-CORE.mdPagination, LRO polling, rate limiting
Gotchas & TroubleshootingCOMMON-CLI.mdaz rest audience, shell escaping, token expiry

Rules

MUST
  • Select exactly one mode from the table above before doing anything else.

  • Read references/<mode>.md end to end, as your FIRST tool call, before the first command of that mode. Read it ONCE, in a single full read: do not re-open it, do not grep it again, and do not page through it. You already have it.

  • Apply the same read-once discipline to every other file you load, including nested references/<mode>/*.md sub-references and shared common/*.md files: open only the ones you actually need, read each ONCE end to end, and never grep or re-open one you already loaded.

  • Resolve workspace and item ids by listing and filtering, never by guessing a GUID.

  • Announce a mode switch explicitly when the request crosses a boundary.

  • Treat the reference as instructions, never as the deliverable. After reading it, RUN the documented commands against the live workspace and report the real results. Quoting what the reference says instead of executing it does not answer the request.

PREFER
  • The narrowest mode that satisfies the request.

  • Reading exactly ONE mode reference. Load a second only when the request genuinely spans modes, and say so before you do.

  • Reporting the mode you chose in your first response so the user can correct you.

AVOID
  • Acting from this dispatcher alone -- it intentionally omits the operational detail.

  • Answering with a summary of the reference instead of executing it.

  • Re-reading or re-grepping a reference you already loaded; it costs turns and tokens.

  • Mutating anything while in a read-only mode.

  • Loading a different skill for work this family already owns (see CRITICAL NOTES 3).

Examples

User requestModeReference to read
"How many events arrived per hour in the last day in the Telemetry eventhouse?"consumptionreferences/consumption.md
"Create a KQL table for the sensor feed and a 30-day retention policy."authoringreferences/authoring.md
"Show me the tables in the KQL database and their row counts."consumptionreferences/consumption.md

Files

references/authoring-core.md

authoring-core.md — KQL Management Command Patterns for Fabric Eventhouse

Contents

  • Authoring Capability Matrix

  • Table Management and Schema Evolution

  • Ingestion and Data Mappings

  • Policies

  • Materialized Views

  • Stored Functions and Update Policies

  • External Tables

  • Permission Model

  • Authoring Gotchas and Troubleshooting Reference

Purpose: Shared reference for Eventhouse authoring skills. Covers table management, ingestion, policies, materialized views, functions, and schema evolution for Fabric Eventhouse and KQL Databases. Not a tutorial — assumes familiarity with KQL management commands. Focus is Fabric-specific behaviour and CLI-driven workflows.


Authoring Capability Matrix

Connection: identical to consumption mode. Management commands (.create, .alter, .drop) require Admin or Ingestor database-level roles.

CapabilityKQL Database (Eventhouse)Shortcut (OneLake)
Create/alter tables✅❌ Read-only
Ingest data✅❌
Retention/caching policies✅❌
Update policies✅❌
Materialized views✅❌
Stored functions✅❌
External tables✅❌
Streaming ingestion✅❌
Data mappings✅❌
Continuous export✅❌

Table Management and Schema Evolution

Create Table
.create table Events (
    Timestamp: datetime, EventType: string,
    UserId: string, Properties: dynamic, Duration: real
)
Create-Merge Table (Safe Schema Evolution)

Adds columns if missing; never drops existing columns. Preferred for idempotent deployments.

.create-merge table Events (
    Timestamp: datetime, EventType: string, UserId: string,
    Properties: dynamic, Duration: real,
    Region: string       // new column — added safely
)
Alter / Rename / Drop
.alter-merge table Events (NewColumn: string)       // add column
.rename column Events.OldName to NewName             // rename column
.drop column Events.OldColumn                        // drop column (irreversible)
.drop table Events ifexists                          // drop table
Schema Evolution
.rename table OldName to NewName

// Atomic swap (Blue-Green pattern)
.rename tables NewEvents = Events, Events_Backup = Events_Old, Events = NewEvents

Ingestion and Data Mappings

Inline Ingestion (Small Data / Testing)
.ingest inline into table Events <|
2025-01-15T10:00:00Z,Login,user1,{},0.5
2025-01-15T10:01:00Z,Click,user2,{"page":"home"},0.2
Set-or-Append / Set-or-Replace
// Append query results
.set-or-append Events <|
    OtherTable
    | where Timestamp > ago(1d)
    | project Timestamp, EventType, UserId, Properties, Duration

// Replace table content with query results
.set-or-replace Events <|
    StagingEvents | where IsValid == true
Ingest from Storage (Blob / ADLS / OneLake)
// From Blob Storage with CSV mapping
.ingest into table Events (
    h'https://myaccount.blob.core.windows.net/container/events.csv.gz;impersonate'
) with (format="csv", ingestionMappingReference="EventsCsvMapping", ignoreFirstRecord=true)

// From OneLake
.ingest into table Events (
    h'abfss://[email protected]/lakehouse.Lakehouse/Files/events.parquet;impersonate'
) with (format="parquet")
Streaming Ingestion

Enable via policy first; data is then ingested via the streaming endpoint or SDKs.

.alter table Events policy streamingingestion enable
Data Mappings
CSV Mapping
.create table Events ingestion csv mapping "EventsCsvMapping"
'['
'  {"column":"Timestamp",  "datatype":"datetime", "ordinal":0},'
'  {"column":"EventType",  "datatype":"string",   "ordinal":1},'
'  {"column":"UserId",     "datatype":"string",   "ordinal":2},'
'  {"column":"Properties", "datatype":"dynamic",  "ordinal":3},'
'  {"column":"Duration",   "datatype":"real",     "ordinal":4}'
']'
JSON Mapping
.create table Events ingestion json mapping "EventsJsonMapping"
'['
'  {"column":"Timestamp",  "path":"$.timestamp",  "datatype":"datetime"},'
'  {"column":"EventType",  "path":"$.eventType",  "datatype":"string"},'
'  {"column":"UserId",     "path":"$.userId",     "datatype":"string"},'
'  {"column":"Properties", "path":"$.properties", "datatype":"dynamic"},'
'  {"column":"Duration",   "path":"$.duration",   "datatype":"real"}'
']'
Show Mappings
.show table Events ingestion csv mappings
.show table Events ingestion json mappings

Policies

Retention Policy
// Table-level: 365-day retention
.alter table Events policy retention
```{"SoftDeletePeriod":"365.00:00:00","Recoverability":"Enabled"}```

// Database-level default (applies to tables without override)
.alter database MyDatabase policy retention
```{"SoftDeletePeriod":"90.00:00:00","Recoverability":"Enabled"}```
Caching Policy

Controls how much data is kept in hot cache (SSD) vs cold storage.

.alter table Events policy caching hot = 30d
.alter database MyDatabase policy caching hot = 7d    // database default
Partitioning Policy
.alter table Events policy partitioning

{ "PartitionKeys": [ { "ColumnName": "UserId", "Kind": "Hash", "Properties": { "Function": "XxHash64", "MaxPartitionCount": 64 } } ] }

Merge Policy
.alter table Events policy merge
```{"RowCountUpperBoundForMerge": 1000000, "MaxExtentsToMerge": 20}```

Materialized Views

Pre-computed aggregations maintained incrementally as new data arrives.

// Create (with optional backfill of existing data)
.create materialized-view with (backfill=true) EventCounts on table Events {
    Events
    | summarize Count = count(), LastSeen = max(Timestamp) by EventType
}

// Alter
.alter materialized-view EventCounts on table Events {
    Events
    | summarize Count = count(), LastSeen = max(Timestamp), FirstSeen = min(Timestamp) by EventType
}

// Lifecycle
.show materialized-view EventCounts statistics
.disable materialized-view EventCounts
.enable materialized-view EventCounts
.drop materialized-view EventCounts

Supported aggregations: count(), sum(), min(), max(), dcount(), avg(), countif(), sumif(), arg_max(), arg_min(), make_set(), make_list(), percentile(), take_any().


Stored Functions and Update Policies

Stored Functions
.create-or-alter function with (
    docstring = "Get events for a specific user in time range",
    folder = "Analytics"
) GetUserEvents(userId: string, lookback: timespan = 1d) {
    Events
    | where Timestamp > ago(lookback) and UserId == userId
    | sort by Timestamp desc
}

.show function GetUserEvents
.drop function GetUserEvents

// Usage
GetUserEvents("user123", 7d) | count
Update Policies

Automatically transform data upon ingestion into a source table.

// Target table
.create table ParsedEvents (
    Timestamp: datetime, EventType: string, UserId: string, PageName: string
)

// Transformation function
.create-or-alter function ParseRawEvents() {
    RawEvents
    | extend Parsed = parse_json(RawData)
    | project
        Timestamp = todatetime(Parsed.timestamp),
        EventType = tostring(Parsed.eventType),
        UserId = tostring(Parsed.userId),
        PageName = tostring(Parsed.properties.page)
}

// Attach update policy
.alter table ParsedEvents policy update
@'[{"IsEnabled":true,"Source":"RawEvents","Query":"ParseRawEvents()","IsTransactional":true}]'

External Tables

OneLake / ADLS External Table
.create external table ExternalSales (
    OrderDate: datetime,
    ProductId: string,
    Quantity: int,
    Amount: real
) kind=storage
dataformat=parquet
(
    h'abfss://[email protected]/lakehouse.Lakehouse/Tables/sales;impersonate'
)
Query External Table
external_table("ExternalSales")
| where OrderDate > ago(30d)
| summarize TotalAmount = sum(Amount) by ProductId
| top 10 by TotalAmount desc

Permission Model

Database Roles
RoleCan QueryCan IngestCan Admin
viewer✅❌❌
user✅❌❌
ingestor❌✅❌
admin✅✅✅
Grant Permissions
// Add a viewer
.add database MyDatabase viewers ('[email protected]')

// Add an admin
.add database MyDatabase admins ('[email protected]')

// Add table-level admin
.add table Events admins ('[email protected]')

// Show current principals
.show database MyDatabase principals

Authoring Gotchas and Troubleshooting Reference

#SymptomCauseFix
1.create table fails with "already exists"Table existsUse .create table ifnotexists or .create-merge table
2Ingestion succeeds but table emptyData mapping mismatchCheck .show table T ingestion csv mappings; verify ordinals/paths match source
3Update policy not triggeringPolicy disabled or function errorCheck .show table T policy update; test function independently
4Materialized view stuck at 0%Source table has no new data or backfill pendingCheck .show materialized-view MV statistics
5.drop column failsColumn used in materialized view or functionRemove dependent objects first
6Retention deleting data too soonTable-level policy overrides database defaultCheck .show table T policy retention
7Forbidden (403) on management commandsInsufficient roleRequest admin or ingestor database role
8Streaming ingestion errorsStreaming policy not enabledRun .alter table T policy streamingingestion enable
9Ingest from OneLake fails with auth errorMissing impersonate in URIAdd ;impersonate to storage URI
10External table returns no dataPath or format mismatchVerify path, format, and schema match the source files

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

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Attribution and licence

Published by
Microsoft
Publisher
Microsoft (Official)
Source
microsoft/skills-for-fabric · skills/eventhouse-cli · commit f8fac11
Licence
MIT License (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced
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