synapse-migration

Update Check — ONCE PER SESSION (mandatory) The first time this skill is used in a session, run the check-updates skill before proceeding.

• GitHub Copilot CLI / VS Code: invoke the check-updates skill.
• Claude Code / Cowork / Cursor / Windsurf / Codex: compare local vs remote package.json version.
• Skip if the check was already performed earlier in this session.

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. mssparkutils and notebookutils share the same API surface in most cases — the namespace is the primary change
4. Linked Services have no direct REST API equivalent in Fabric — they are replaced by Data Connections (for external sources) and OneLake Shortcuts (for storage mounts)

Synapse Analytics → Microsoft Fabric Migration

Prerequisite Knowledge

Read these companion documents before executing migration tasks:

• COMMON-CORE.md — Fabric REST API patterns, authentication, token audiences, item discovery
• COMMON-CLI.md — az rest, az login, token acquisition, Fabric REST via CLI
• SPARK-AUTHORING-CORE.md — Notebook deployment, lakehouse creation, Spark job execution
• SQLDW-AUTHORING-CORE.md — Fabric Warehouse T-SQL authoring, DDL, COPY INTO

For notebook deployment details, see spark-authoring-cli. For Fabric Warehouse DDL/DML authoring, see sqldw-authoring-cli.

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Table of Contents

Topic	Reference
Migration Workload Map	§ Migration Workload Map
mssparkutils → notebookutils API Mapping	utility-api-mapping.md
Linked Services → Data Connections / Shortcuts	connectivity-migration.md
Before/After Code Patterns	code-patterns.md
T-SQL Surface Area Gaps	§ T-SQL Surface Area Gaps
Spark Configuration Differences	§ Spark Configuration Differences
Must / Prefer / Avoid	§ Must / Prefer / Avoid
Authentication & Token Acquisition	COMMON-CORE.md § Authentication
Lakehouse Management	SPARK-AUTHORING-CORE.md § Lakehouse Management
Notebook Management	SPARK-AUTHORING-CORE.md § Notebook Management
Fabric Warehouse Authoring	SQLDW-AUTHORING-CORE.md

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Migration Workload Map

Use this table to determine the correct Fabric target for each Synapse component:

Synapse Component	Fabric Target	Notes
Spark Pool (notebooks, jobs)	Fabric Spark (Lakehouse / Notebooks / SJD)	Starter Pool replaces on-demand pools for most workloads
Dedicated SQL Pool	Fabric Warehouse	T-SQL surface area differences apply — see § T-SQL Surface Area Gaps
Serverless SQL Pool	Lakehouse SQL Endpoint	Read-only Delta/Parquet queries; no DDL required
Synapse Pipelines	Fabric Data Pipelines	Activity types, triggers, and expressions are broadly compatible
Synapse Link for Cosmos DB / SQL	Fabric Mirroring	Native mirroring replaces the Synapse Link connector pattern
Linked Services	Data Connections (external) / OneLake Shortcuts (storage)	See connectivity-migration.md
Integration Datasets	Fabric Pipeline source/sink config	Dataset definitions are inlined into pipeline activities in Fabric
Managed Virtual Networks	Fabric Managed Private Endpoints	Configure in Fabric capacity settings
Synapse Studio	Fabric workspace	All artifact types live in a single workspace with Git integration

Decision Tree: Which Fabric Spark Workload?

Synapse Spark workload
├── Interactive notebook with data exploration → Fabric Notebook (attached to Lakehouse)
├── Scheduled/production job → Spark Job Definition (SJD)
├── T-SQL over files/Delta → Lakehouse SQL Endpoint (no migration needed — just point to OneLake)
└── Real-time ingest → Fabric Eventstream + Lakehouse

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T-SQL Surface Area Gaps

Fabric Warehouse supports a broad T-SQL surface, but some Dedicated SQL Pool features differ:

Synapse Dedicated SQL Pool Feature	Fabric Warehouse Equivalent	Action Required
CREATE EXTERNAL TABLE (PolyBase)	COPY INTO or Lakehouse SQL Endpoint	Rewrite ingestion; use COPY INTO for bulk load from ADLS/OneLake
DISTRIBUTION = HASH(col)	Not applicable — Fabric auto-distributes	Remove distribution hints from DDL
CLUSTERED COLUMNSTORE INDEX (default)	Delta Lake (Lakehouse) or Fabric Warehouse DCI	Warehouse tables use Delta-backed storage automatically
Result set caching	Not available	Remove cache hints; rely on query plan caching
Workload management (classifiers)	Not available	Use workspace capacity management
sp_rename	Supported	No change needed
MERGE statement	Supported	No change needed
Temp tables (#temp)	Supported	No change needed
Window functions	Supported	No change needed

Delegate to sqldw-authoring-cli for all T-SQL DDL/DML authoring tasks after mapping the workload.

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Spark Configuration Differences

Synapse Spark Concept	Fabric Spark Equivalent	Notes
Spark Pool definition (node type, autoscale min/max)	Custom Pool or Starter Pool	Starter Pool (auto-provisioned, no config needed) covers most dev workloads; Custom Pools for production SLAs
%%configure magic cell (session-level config)	%%configure magic — identical syntax	Supported in Fabric notebooks
spark.conf.set(...)	spark.conf.set(...) — identical	No change needed
Environment-scoped libraries (pool packages)	Fabric Environment attached to workspace/notebook	Replace pool-level library installs with a Fabric Environment item
Synapse-specific Spark versions	Fabric Runtime versions (1.1 = Spark 3.3, 1.2 = Spark 3.4, 1.3 = Spark 3.5)	Align runtime version; test deprecated API calls
spark.read.synapsesql(...) connector	Not available — use notebookutils + Lakehouse shortcuts or Warehouse JDBC	Replace with OneLake reads or SQL endpoint queries

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Must / Prefer / Avoid

MUST DO

• Replace all mssparkutils imports with notebookutils — see utility-api-mapping.md for the complete namespace table
• Replace all Linked Services with Fabric Data Connections (for external databases/services) or OneLake Shortcuts (for ADLS Gen2 / Blob storage mounts) — see connectivity-migration.md
• Replace spark.read.synapsesql() with Lakehouse shortcut reads or JDBC connections to the Fabric Warehouse SQL endpoint
• Re-test all notebooks after migration against the target Fabric Runtime version — Spark minor version differences can surface deprecated API warnings
• Externalize all workspace/item IDs — never hardcode; use pipeline parameters or Variable Libraries
• Replace pool-level library installs with Fabric Environments attached at the workspace or notebook level

PREFER

• OneLake Shortcuts over full data copies — mount existing ADLS Gen2 containers as shortcuts rather than re-ingesting data during migration
• Fabric Starter Pool for dev/test migrations — eliminates pool warm-up wait time inherent in Synapse on-demand pools
• Lakehouse SQL Endpoint as a drop-in for Serverless SQL Pool reads — point existing consumers at the endpoint with minimal query changes
• Medallion architecture for migrated data — align with Bronze/Silver/Gold patterns (see e2e-medallion-architecture skill)
• Incremental migration — migrate and validate workload by workload rather than performing a big-bang cutover
• Parameterized notebooks to allow environment promotion (dev → test → prod) without code changes

AVOID

• Do not copy-paste PolyBase CREATE EXTERNAL TABLE DDL into Fabric Warehouse — rewrite as COPY INTO or use Lakehouse for external data access
• Do not assume Synapse Linked Service connection strings are reusable — credentials and endpoints must be reconfigured as Fabric Data Connections
• Do not install libraries in notebook cells (%pip install at runtime) for production workloads — use Fabric Environments for reproducible, versioned library management
• Do not migrate Dedicated SQL Pool distribution hints (HASH, ROUND_ROBIN, REPLICATE) verbatim — remove them; Fabric Warehouse handles distribution automatically
• Do not use wasb:// or abfss://container@storageaccount.dfs.core.windows.net/ paths as primary data paths — migrate data access to OneLake abfss://workspace@onelake.dfs.fabric.microsoft.com/ paths

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Examples

See code-patterns.md for full before/after examples. Key quick references:

mssparkutils.env → notebookutils.runtime

# Synapse
workspace = mssparkutils.env.getWorkspaceName()

# Fabric
workspace = notebookutils.runtime.context["workspaceName"]

Linked Service credential → Key Vault secret

# Synapse
conn = mssparkutils.credentials.getConnectionStringOrCreds("MyLinkedService")

# Fabric
conn = notebookutils.credentials.getSecret("https://myvault.vault.azure.net/", "my-secret")

Dedicated SQL Pool DDL → Fabric Warehouse DDL

-- Synapse (remove distribution hints)
CREATE TABLE dbo.Fact (...) WITH (DISTRIBUTION = HASH(id), CLUSTERED COLUMNSTORE INDEX);

-- Fabric Warehouse
CREATE TABLE dbo.Fact (...);