synapse-migration

作者: microsoft

更新檢查 — 每次工作階段一次(強制) 此技能在一個工作階段中首次使用時,請先執行檢查更新技能再繼續。

npx skills add https://github.com/microsoft/skills-for-fabric --skill synapse-migration

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

OFFLINE LAKEHOUSE ARTIFACT FAST PATH — DECIDE FIRST. For a no-live-call request with complete supplied inputs and contracts, load only the fast path; do not load migration-planning-reference.md, dedicated-pool-conversion.md, or implementation scripts. Use one generator for artifacts and final-byte hashes, then one verifier. Do not inspect generated files or run post-pass spot checks. For every optional non-null source default, set missingValueBehavior to UseSourceDefault and restore that default before validation. Do not use this path for a Warehouse target; incomplete and large-procedure requests use their own routes.

CRITICAL NOTES

  1. To find workspace details (including its ID) from a workspace name: list all workspaces, then use JMESPath filtering.
  2. To find item details (including its ID) from workspace ID, item type, and item name: list all items of that type in that workspace, then use JMESPath filtering.
  3. Treat a source Dedicated SQL Pool as read-only. Do not run source DDL, DML, stored procedures, or create sample objects.
  4. Dedicated Pool-to-Lakehouse data movement is out of scope. Migrate schema and executable code artifacts only; do not copy source rows or run row-level/business-result equivalence queries. The separate Warehouse route may move data only from a separately approved restored export copy under its own consent gate and restored-source contract.
  5. For Dedicated Pool-to-Lakehouse requests, complete gap assessment, explicit mapping approval, manifest creation, conversion, deployment, and validation in order. Read dedicated-pool-to-lakehouse.md once as the router, then load only the current phase reference.
  6. Stored-procedure 1:1, N:1, and N:N mappings never apply to views. Views remain schema objects; procedures become traceable Spark SQL notebooks with executable %%sql transformation cells.
  7. Dependent ADF/Synapse callers are conditional scope, not automatic scope. Discover them during assessment, require a separate complete-closure approval, and migrate them only after notebook readiness. Route standalone pipeline requests to pipeline-migration.
  8. Before executing a specialized Dedicated Pool workflow, read the matching contract in dedicated-pool-execution-contracts.md. Its validation, hashing, request-body, resume, and completion-language requirements are mandatory.
  9. Keep Lakehouse and Warehouse paths separate. If the target is unclear, ask before loading path-specific resources. Lakehouse conversion never moves source rows; Warehouse data migration requires its own explicit consent gate and restored-source contract.
  10. For a Fabric Warehouse target, explicitly state that the complete DISTRIBUTION and CLUSTERED COLUMNSTORE INDEX declarations are removed because Fabric manages distribution, storage, and indexing; a bare mention of either source clause is insufficient.
  11. Run setup DDL and CETAS only against the separately approved restored export copy after proving it is not the original production source.

Synapse Analytics to Microsoft Fabric Migration

Core Workflow

  1. Discover the Synapse workspace and classify every source artifact.
  2. Choose targets using workload-routing.md.
  3. Assess gaps before conversion, including runtime, library, connectivity, T-SQL, security, and capacity constraints.
  4. Obtain approval for behavior-changing decisions, target placement, and every stored-procedure-to-notebook mapping.
  5. Create or update migration-manifest.json and persist approval, per-object status, hashes, and checkpoints atomically.
  6. Convert and deploy in dependency order, loading only the current phase resource.
  7. Validate persisted definitions and artifacts without executing generated notebooks or pipelines unless a separately approved workflow explicitly requires execution.
  8. Report completed, blocked, skipped, and pending objects without exposing credentials or sensitive values.

For full-workspace orchestration, read migration-orchestrator.md. For a Dedicated Pool-to-Lakehouse migration, use dedicated-pool-to-lakehouse.md as the phase router. For a Warehouse target, load only the matching dw-* resource.

Scope Routing

RequestActionLoad
Full Synapse workspace migrationRun phased discovery and migrationmigration-orchestrator.md
Dedicated SQL Pool schema/code to LakehouseRun the strict assessment-to-validation workflow. For guidance-only notebook publication, explicitly state: "The stored procedures were converted from T-SQL to Spark SQL before publication."dedicated-pool-to-lakehouse.md
Guidance-only Dedicated Pool incremental deployment hardeningAnswer directly from the focused checklist; do not search scripts or load phase resources. State: "For columns with special characters, enable Delta columnMapping selectively and only with approval."dedicated-pool-execution-contracts.md
Large or complex stored procedureFor a complete offline contract, read only the audit resource once, use one run-local generator and one verifier, do not inspect implementation scripts or generated outputs, and stop after successful verificationdedicated-pool-large-procedure-audit.md
Spark poolConvert configuration and libraries to a Fabric Environmentspark-pool-migration.md
Lake Database or HMSCreate or bind the target Lakehouselake-database-migration.md or external-hms-migration.md
Notebook or Spark Job DefinitionPort code, bindings, parameters, and runtime settingsspark-item-migration.md
Linked ServiceChoose a Fabric Data Connection for external services or a OneLake Shortcut for ADLS Gen2/Blobconnectivity-migration.md
Approved dependent procedure callersRun the internal closure-preserving sub-flow after notebook readbackdedicated-pool-dependent-pipelines.md
Standalone pipeline migrationDelegatepipeline-migration
Dedicated SQL Pool to Fabric WarehouseRun the source extraction, DDL compatibility, approval, optional data, security, and validation routedw-source-and-extraction.md, then the matching dw-* phase resource
Standalone Fabric Warehouse administrationDelegatesqldw-cli
Synapse LinkUse Fabric Mirroring; not covered here—

Always report the Lake Database/HMS phase. If none is discovered, mark it NotApplicable (none discovered) while retaining the required target Lakehouse binding.

Load References On Demand

Do not preload all files. Load only the references needed for the current phase:

NeedReference
Routing, phase order, API audiences, quick examples, troubleshooting, or post-migration handoffworkload-routing.md
Dedicated Pool execution variants and response contractsdedicated-pool-execution-contracts.md
Dedicated Pool discoverydedicated-pool-discovery.md
Gap assessment and mapping approvaldedicated-pool-gap-assessment.md
Procedure conversion and notebook shapededicated-pool-conversion.md
Schema/notebook deploymentdedicated-pool-deployment.md
Dedicated Pool validationdedicated-pool-validation.md
Runtime/library compatibilityfeature-parity.md and library-compatibility.md
mssparkutils and connector refactoringutility-api-mapping.md, connector-refactoring.md, and code-patterns.md
Capacity planningcapacity-sizing.md
Dedicated Pool-to-Warehouse source discovery and extractiondw-source-and-extraction.md
Warehouse DDL compatibility and deploymentdw-ddl-compatibility.md
Separately approved Warehouse data migrationdw-data-migration.md
Warehouse security and validationdw-security-validation.md
Validation, reporting, and production readinessvalidation-testing.md, migration-report.md, and security-governance.md

Use COMMON-CLI.md only for shared authentication and az rest mechanics not already covered by the phase resource. Use COMMON-CORE.md only for shared Fabric REST patterns.

Must

  • Preserve every stored-procedure input contract, source default, source decision, and approved target mapping.
  • Block conversion until gap dispositions, notebook names, dependency grouping, and target workspace placement are explicitly approved.
  • Use SqlPackage/DMVs for source discovery, Fabric REST for item management, and Fabric Livy statements for Lakehouse schema and Delta execution.
  • Run the maintained validators required by the selected execution contract; never replace them with ad hoc JSON or cell-shape checks.
  • Choose the Lakehouse or Warehouse target before loading implementation resources; never apply Warehouse data-movement rules to Lakehouse artifact conversion.
  • Before persisting any dependent-pipeline source definition, recursively inspect it for secret-bearing or uncertain properties; block the complete selected closure without writing the artifact or mutating a target when any are found.
  • In every dependent-pipeline response, including guidance-only and pre-approval explanations, state: "Use a TridentNotebook activity with exact target identities typeProperties.notebookId and typeProperties.workspaceId, and pass inputs through typeProperties.notebookParameters." Then state: "After deployment, read back each pipeline with POST /v1/workspaces/{workspaceId}/items/{dataPipelineId}/getDefinition and validate its canonical hash and structural bindings."
  • For every Linked Service response, first state both replacement classes: Fabric Data Connections for external databases/services and OneLake Shortcuts for ADLS Gen2/Blob storage; then identify which class applies.
  • Replace mssparkutils with notebookutils, Linked Services with the correct Fabric connection class, and spark.read.synapsesql() with a supported OneLake or JDBC pattern.
  • Externalize workspace and item IDs through parameters or Variable Libraries.
  • Keep phase status concise, emit bounded heartbeats for long operations, and persist per-object evidence in the manifest.

Prefer

  • OneLake Shortcuts over copying data.
  • Fabric Environments for reproducible libraries and Spark settings.
  • Incremental, dependency-ordered migration over big-bang cutover.
  • Parameterized notebooks and Variable Libraries for environment promotion.
  • Lakehouse SQL Endpoint for compatible Serverless SQL Pool read workloads.

Avoid

  • Do not execute generated notebooks or pipelines as part of artifact migration.
  • Do not broaden a selected dependent-pipeline closure or route that internal sub-flow to pipeline-migration.
  • Do not use sqlcmd against the target Fabric Warehouse; use the registered SQL endpoint tooling required by the Warehouse resource.
  • Do not preserve Synapse distribution/storage hints, PolyBase DDL, Linked Service connection strings, or pool-level library installation patterns verbatim.
  • Do not use target notebooks to orchestrate the migration itself.
  • Do not hardcode workspace IDs, item IDs, credentials, connection strings, or environment-specific paths.

Examples

Prompt: "Migrate this Dedicated SQL Pool DACPAC to a Fabric Lakehouse and convert the procedures."

Response behavior: Assess gaps first, present and obtain approval for the complete procedure-to-notebook mapping and workspace placement, create the manifest, convert approved schema/code artifacts without moving source rows, validate persisted definitions, and report the resolved Lakehouse and artifact evidence.

Prompt: "Migrate this ADF pipeline to Fabric."

Response behavior: Route to pipeline-migration. Keep it here only when the pipeline is an explicitly approved dependent caller discovered inside an active Dedicated Pool-to-Lakehouse migration.

Prompt: "Migrate this Dedicated SQL Pool to Fabric Warehouse."

Response behavior: Keep this inside synapse-migration, assess DDL compatibility, remove unsupported distribution and columnstore declarations, obtain provisioning consent, and ask separately before any restored-source data migration.

For code transformations and edge cases, load workload-routing.md and the phase-specific resource rather than expanding the main instructions.

來自 microsoft 的更多技能

oss-growth
microsoft
開源增長駭客角色
agent-framework-azure-ai-py
microsoft
使用Microsoft Agent Framework Python SDK(agent-framework-azure-ai)构建Azure AI Foundry代理。适用于使用AzureAIAgentsProvider创建持久化代理、使用托管工具(代码解释器、文件搜索、网络搜索)、集成MCP服务器、管理对话线程或实现流式响应。涵盖函数工具、结构化输出和多工具代理。
development
airunway-aks-setup
microsoft
在AKS上設定AI Runway——從裸叢集到執行模型。涵蓋叢集驗證、控制器安裝、GPU評估、供應商設定及首次部署。時機:「設定AI Runway」、「上線AKS叢集」、「安裝AI Runway」、「airunway設定」、「部署模型至AKS」、「在AKS上進行GPU推論」、「在AKS上設定KAITO」、「在AKS上執行LLM」、「在AKS上使用vLLM」、「在AKS上設定模型服務」、「AI Runway控制器」。
devops
appinsights-instrumentation
microsoft
使用Azure Application Insights檢測Web應用程式的指南。提供遙測模式、SDK設定與組態參考。適用時機:如何檢測應用程式、App Insights SDK、遙測模式、什麼是App Insights、Application Insights指南、檢測範例、APM最佳實踐。
devops
applicationinsights-web-ts
microsoft
使用Application Insights JavaScript SDK(@microsoft/applicationinsights-web)為瀏覽器/Web應用程式進行檢測。適用於真實使用者監控(RUM)——頁面檢視、點擊、AJAX/fetch依賴、例外、自訂事件,以及與後端OpenTelemetry追蹤關聯的瀏覽器端GenAI代理追蹤。涵蓋SDK載入器指令碼與npm設定、框架擴充(React、React Native、Angular)、點擊分析、遙測初始化器,以及從瀏覽器發出的代理/工具/模型span的OTel GenAI語意慣例。
devops
azure-ai-anomalydetector-java
microsoft
使用適用於 Java 的 Azure AI 異常偵測器 SDK 建置異常偵測應用程式。在實作單變量/多變量異常偵測、時間序列分析或 AI 驅動監控時使用。
development
azure-ai-language-conversations-py
microsoft
使用 azure-ai-language-conversations Python SDK 實作對話語言理解(CLU)。當使用 ConversationAnalysisClient 分析對話意圖與實體、建置 NLP 功能,或將語言理解整合至應用程式時使用。
development
azure-ai-ml-py
microsoft
Azure Machine Learning SDK v2 for Python。用於機器學習工作區、作業、模型、資料集、計算資源與管線。 觸發詞:「azure-ai-ml」、「MLClient」、「workspace」、「model registry」、「training jobs」、「datasets」。
development