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Synapse Migration

MicrosoftOfficialPublished by Microsoft

  • Skill
  • Workflow
  • Software development
  • MIT
  • Low risk
  • Has scripts

From the author

Converts Synapse workspaces to Fabric: Spark items, Lake Databases, Linked Services, and Dedicated SQL Pool schema/procedures for Lakehouse or Warehouse. During Pool-to-Lakehouse migration only, can migrate approved dependent procedure callers after notebook readiness. Excludes standalone pipelines and Warehouse administration. Triggers: Synapse workspace to Fabric, Dedicated SQL Pool to Lakehouse, Dedicated SQL Pool to Fabric Warehouse.
— the description by Microsoft

AutoGineer summary

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

The synapse-migration skill provides a structured, phased workflow for transitioning Synapse Analytics workspaces to Microsoft Fabric. It covers the migration of Spark items, Lake Databases, Linked Services, and Dedicated SQL Pool schema and stored procedures. The skill emphasizes a dependency-ordered approach, requiring gap assessment and explicit approval before conversion. It distinguishes between migrating to a Fabric Lakehouse (schema and code only, no data movement) and a Fabric Warehouse, while providing specific routing for dependent pipeline callers.

What it does

  • Discovers and classifies Synapse workspace artifacts.
  • Converts Dedicated SQL Pool schema and stored procedures to Spark SQL notebooks or Warehouse objects.
  • Migrates Spark items, including notebooks and Spark Job Definitions, to Fabric equivalents.
  • Maps Linked Services to Fabric Data Connections or OneLake Shortcuts.
  • Manages the migration of approved dependent pipeline callers.
  • Validates persisted definitions and artifacts against established contracts.

When to use it

  • When migrating a full Synapse workspace to Microsoft Fabric.
  • When converting Dedicated SQL Pool schema and procedures to a Lakehouse or Warehouse.
  • When porting Spark pools, notebooks, or Spark Job Definitions to Fabric.
  • When replacing Synapse Linked Services with Fabric-native connectivity.

Advantages

  • Ensures dependency-ordered migration to maintain system integrity.
  • Provides clear routing for different workload types (Lakehouse vs. Warehouse).
  • Enforces strict validation and approval gates for behavior-changing decisions.
  • Supports incremental migration rather than high-risk big-bang cutovers.
  • Standardizes the conversion of T-SQL procedures to executable Spark SQL notebooks.

Usage examples

“Migrate this Dedicated SQL Pool DACPAC to a Fabric Lakehouse and convert the procedures.”
The agent will assess gaps, obtain approval for procedure-to-notebook mappings, create a migration manifest, convert schema/code artifacts, and validate the results.
“Migrate this Dedicated SQL Pool to Fabric Warehouse.”
The agent will assess DDL compatibility, remove unsupported distribution/index declarations, obtain provisioning consent, and prepare for potential data migration.

Install

  1. Download the skill as a ZIP:

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

    mkdir -p .agents/skills && unzip synapse-migration.zip -d .agents/skills

Documentation by Microsoft

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.

Files

resources/dw-source-and-extraction.md

Source Resolution and Metadata Extraction

Load this resource for Steps 1-2 only.

Resolve the source

Normalize both source modes to these values:

<SOURCE_SQL_SERVER>
<SOURCE_DATABASE>
Synapse workspace mode

Required inputs: Azure subscription ID, Synapse workspace name, and one explicit pool name. List workspaces and pools and validate an exact pool match. If it is paused, stop and ask the user to resume it through a separately approved operator workflow; never resume it automatically. Then set:

<SOURCE_SQL_SERVER> = <SYNAPSE_WORKSPACE_NAME>.sql.azuresynapse.net
<SOURCE_DATABASE>   = <POOL_NAME>

Use Synapse ARM API version 2021-06-01. Do not default to all pools.

Standalone mode

Required inputs: SQL server FQDN and database name from a redacted connection string. Remove an optional tcp: prefix and port suffix. Never request or persist passwords. Set:

<SOURCE_SQL_SERVER> = <user-provided-server-fqdn>
<SOURCE_DATABASE>   = <user-provided-database-name>

Skip Synapse workspace discovery. Ensure the database is online through its owning Azure service.

Select source authentication and validate connectivity

Support both source authentication methods:

  • Microsoft Entra (default): use sqlcmd -G and the authenticated Azure identity.

  • SQL authentication: use a supplied SQL login name. Never request, print, log, or persist the password. Run sqlcmd without -P so it prompts for the password directly in the terminal, or use an equivalent secure client prompt.

Do not place passwords in command arguments, environment files, generated SQL, reports, or chat prompts. Fabric Warehouse target connections always use Microsoft Entra authentication.

Entra validation:

sqlcmd -S "<SOURCE_SQL_SERVER>" -d "<SOURCE_DATABASE>" -G -Q "SELECT DB_NAME();"

SQL authentication validation:

sqlcmd -S "<SOURCE_SQL_SERVER>" -d "<SOURCE_DATABASE>" -U "<SOURCE_SQL_USER>" -Q "SELECT DB_NAME();"

Allow the terminal to prompt securely for the password.

Stop unless the returned database exactly matches <SOURCE_DATABASE>.

Permission preflight

Before inventory, run read-only permission checks for catalog visibility and definition access, including VIEW DEFINITION and SELECT access to required catalog views. If the source principal cannot see the complete supported object inventory, list the exact missing permissions and stop Steps 2-4. Do not continue with a partial or best-effort metadata inventory.

Resolve metadata scope

Default to all supported objects in the source database:

  • Schemas and tables, including constraints and identity properties

  • Views

  • Stored procedures

  • Scalar, inline table-valued, and table-valued functions

  • External tables

  • External data sources

  • External file formats

  • Database-scoped credential references and access identity metadata

  • Roles, role memberships, object/schema/database permissions

  • Row-level security (RLS), column-level security (CLS), and dynamic data masking (DDM)

Allow narrowing by schema, object type, or typed schema-qualified object name such as view:reporting.SalesSummary. Validate names against source catalogs. Discover dependencies and ask whether to include missing dependencies or mark the dependent object as blocked.

If valid user filters resolve to zero metadata objects, continue automatically as an explicit no-op. Do not fall back to all objects. Record zero selected objects and the applied filters, skip extraction/conversion/deployment work, and produce the assessment/completion report.

Catalog extraction

Use sys.tables, sys.schemas, sys.columns, sys.types, sys.identity_columns, sys.computed_columns, sys.key_constraints, sys.foreign_keys, sys.foreign_key_columns, sys.default_constraints, sys.check_constraints, and partition catalog views to reconstruct table DDL. Always extract computed-column expressions, DEFAULT, CHECK, and partition definitions for tables in scope so each can be assessed.

Use sys.sql_modules joined to sys.objects and sys.schemas for views, procedures, and functions. Include object types V, P, FN, IF, and TF.

Dedicated SQL pool does not support triggers. Do not query sys.triggers, inventory trigger objects, or count triggers as migration blockers.

Build the dependency graph from sys.sql_expression_dependencies before scanning definition text. Resolve referencing_id through sys.objects and sys.schemas. For OBJECT_OR_COLUMN references (referenced_class = 1), resolve a non-null referenced_id through sys.objects and sys.schemas; when referenced_minor_id > 0, also join sys.columns on object_id = referenced_id and column_id = referenced_minor_id to preserve the referenced column. Treat referenced_minor_id = 0 as an object-level dependency, and preserve other referenced classes without forcing them through sys.objects.

Preserve catalog-supplied server, database, schema, and entity names when referenced_id is null. Record caller-dependent, ambiguous, and unresolved local references as assessment blind spots. Record any non-null object or column ID that does not resolve through its required catalog join as an extraction failure.

After catalog dependency extraction, scan definitions for three-part database names, four-part linked-server names, external object calls, and dynamic SQL that constructs remote references. Add these supplemental dependencies with their referencing objects, but do not use text scanning as a substitute for sys.sql_expression_dependencies or its column joins.

Inventory source external objects with explicit read-only catalog queries:

  • Use sys.external_tables joined to sys.schemas, sys.columns, and sys.types for each external table's schema, columns, location, data-source ID, and file-format ID.

  • Query all rows from sys.external_data_sources, then link external tables by data-source ID; capture each source's name, type, location, and nullable credential ID.

  • Query all rows from sys.external_file_formats, then link external tables by file-format ID; capture each format's name and properties.

  • Query all rows from sys.database_scoped_credentials, then link data sources by non-null credential ID only; capture the credential name and identity metadata, but never retrieve, request, or persist its secret.

Preserve unreferenced catalog objects as well as the complete external-table-to-data-source, file-format, and credential reference chain. Record a non-null ID that does not resolve as an extraction failure; a null optional credential ID is valid and is not a failed join. Include dependent objects, and do not treat definition-text scanning as a substitute for these catalogs.

Use these security catalogs:

  • sys.database_principals and sys.database_role_members for roles and memberships; generate custom-role definitions only where type = 'R', is_fixed_role = 0, and name <> 'public'

  • sys.database_permissions; use minor_id joined to sys.columns for CLS

  • sys.security_policies and sys.security_predicates plus predicate function definitions for RLS

  • sys.masked_columns for DDM

Apply the resolved metadata filters to every extraction query. Write one SQL file per object under migration-output/<SOURCE_DATABASE>/ and persist the final resolved scope in migration-report.md.

Ignore source statistics completely. Do not inventory sys.stats, generate statistics scripts, count statistics in reports, or classify them as review items or blockers.

Skip source sequence objects. Do not generate CREATE SEQUENCE scripts or special sequence migration recommendations.

Source-management references

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

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Needs: Needs Python

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Attribution and licence

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