Telemetry — MANDATORY. Every
api.fabric.microsoft.comcall must carryx-ms-fabric-skill: databricks-migration(az rest:--headers "x-ms-fabric-skill=databricks-migration"), including every LRO poll,fabric_lroand retry. Snippets omit it — add it anyway.
CRITICAL NOTES
- To find workspace details (including its ID) from a workspace name: list all workspaces, then use JMESPath filtering
- 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
dbutils.widgetshas no direct equivalent in Fabric — use notebook parameters (cell tagparameters);notebookutils.runtime.contextis execution metadata, not parameter storage. If showing context fields, use documented names such ascurrentWorkspaceId,currentWorkspaceName,currentNotebookId,currentNotebookName,isForPipeline, andisForInteractive;activityIdis the Livy job IDdbutils.library(runtime library install) has no equivalent — use Fabric Environments for reproducible library management- Map each Unity Catalog catalog to a schema-enabled Lakehouse by default. This preserves the source
schema.tablehierarchy, with the Lakehouse representing the catalog; collisions arise only if multiple catalogs are intentionally consolidated into one Lakehouse- For an under-specified workspace-wide migration, ask focused questions about inventory, workload topology, security, data locations, and runtime constraints before recommending a Fabric topology
- A completed Fabric migration must not retain executable
dbutils.*calls in dual-runtime branches ortry/exceptguards — replace the calls and Databricks paths outright
Databricks → 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
For notebook and Lakehouse creation, see spark-cli. For Fabric Warehouse DDL/DML authoring, see sqldw-cli.
Table of Contents
| Topic | Reference |
|---|---|
| Migration Orchestrator | migration-orchestrator.md |
| Migration Workload Map | § Migration Workload Map |
Complete dbutils → notebookutils Mapping | dbutils-to-notebookutils.md |
| Unity Catalog → Fabric Lakehouse Schemas | catalog-migration.md |
| Before/After Code Patterns | code-patterns.md |
| Cluster Config → Fabric Spark Pools | § Cluster Config → Fabric Spark Pools |
| Databricks Jobs → Spark Job Definitions | § Databricks Jobs → Spark Job Definitions |
| Delta Sharing → Fabric External Data Sharing and OneLake Shortcuts | § Delta Sharing → Fabric External Data Sharing and OneLake Shortcuts |
| MLflow → Fabric ML Experiments | § MLflow → Fabric ML Experiments |
| Post-Migration Validation & Testing | validation-testing.md |
| Migration Gotchas & Troubleshooting | migration-gotchas.md |
| Multi-Notebook Migration Protocol | § Multi-Notebook Migration Protocol |
| Failure Reporting | § Failure Reporting |
| 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 |
Context Loading Guide
IMPORTANT — Load only what you need. Do NOT read all resource files upfront. Load the specific file for the phase you are executing:
| When | Read This File |
|---|---|
| User asks to migrate a workspace (full orchestration) | migration-orchestrator.md |
| Applying code transforms (dbutils, namespaces, paths) | dbutils-to-notebookutils.md + code-patterns.md |
| Resolving Unity Catalog namespace collisions | catalog-migration.md |
| Post-migration verification | validation-testing.md |
| Troubleshooting failures or known issues | migration-gotchas.md |
Migration Workload Map
| Databricks Component | Fabric Target | Severity | Notes |
|---|---|---|---|
| All-purpose cluster (notebooks, REPL) | Fabric Notebook (Starter Pool or Custom Pool) | Info | No persistent cluster — Fabric provisions compute on session start |
| Job cluster (automated jobs) | Spark Job Definition (SJD) | Info | SJD maps one-to-one with Databricks Jobs on job clusters |
| Unity Catalog | Fabric Lakehouse (schema-enabled, one per catalog) | Info | Schema-enabled Lakehouse preserves schema.table; default one-Lakehouse-per-catalog has no collision — see catalog-migration.md |
| Databricks Repos (Git-backed notebooks) | Fabric Git Integration | Info | Connect workspace to Azure DevOps or GitHub; notebooks are synced |
| Delta Live Tables (DLT) | Fabric Notebooks + Data Pipelines | Blocker | No DLT equivalent — rewrite DLT datasets as parameterized notebook cells with pipeline orchestration |
| Databricks SQL Warehouses | Fabric Warehouse or Lakehouse SQL Endpoint | Info | SQL warehouse sessions → Warehouse (for write) or SQL Endpoint (for read-only) |
| MLflow Tracking | Fabric ML Experiments | Info | MLflow SDK is supported in Fabric — see § MLflow |
| Delta Sharing | OneLake Shortcuts + Fabric external data sharing | Warning | See § Delta Sharing → Fabric External Data Sharing and OneLake Shortcuts |
| Databricks Feature Store | Feature engineering on Lakehouse/Delta tables + MLflow | Warning | Fabric has no drop-in managed Feature Store; recreate feature tables as Delta tables in a Lakehouse and manage features via notebooks/MLflow. Verify current Fabric feature-store roadmap before committing an approach |
| dbutils (all sub-modules) | notebookutils (most sub-modules) | Info | See dbutils-to-notebookutils.md for full mapping |
| Scala notebooks | Fabric Notebook (Spark/Scala) | Warning | Scala is supported; swap cell magic %scala → %%spark and rewrite Databricks-specific APIs/libraries |
| R notebooks | Fabric Notebook (SparkR) | Warning | SparkR is supported; swap cell magic %r → %%sparkr, validate package availability, rewrite Databricks-specific APIs |
Severity Definitions
| Level | Meaning | Action |
|---|---|---|
| Blocker | Cannot run in Fabric without redesign or user decision | Stop — surface to user, require resolution |
| Warning | Migratable but requires validation or architectural decision | Migrate with review flag |
| Info | Direct substitution-level change | Auto-migrate |
dbutils → notebookutils Quick Reference
The complete side-by-side API table is in dbutils-to-notebookutils.md. The key mappings are:
dbutils Call | notebookutils Equivalent | Compatibility Note |
|---|---|---|
dbutils.fs.ls(path) | notebookutils.fs.ls(path) | Direct replacement |
dbutils.fs.cp(src, dest) | notebookutils.fs.cp(src, dest) | Direct replacement |
dbutils.fs.mv(src, dest) | notebookutils.fs.mv(src, dest, create_path, overwrite=False) | ⚠️ Signature differs — see dbutils-to-notebookutils.md |
dbutils.fs.rm(path, recurse) | notebookutils.fs.rm(path, recurse) | Direct replacement |
dbutils.fs.mkdirs(path) | notebookutils.fs.mkdirs(path) | Direct replacement |
dbutils.fs.put(path, contents) | notebookutils.fs.put(path, contents) | Direct replacement |
dbutils.fs.head(path, maxBytes) | notebookutils.fs.head(path, max_bytes) | ⚠️ Default differs — Python/Scala 100 KB, R 64 KB. See dbutils-to-notebookutils.md |
dbutils.fs.mount(...) | notebookutils.fs.mount(source, mountPoint, extraConfigs=None) | ✅ Supported — Microsoft Entra (default), accountKey, or sasToken auth. For cross-workspace / persistent sharing, prefer OneLake Shortcuts |
dbutils.secrets.get(scope, key) | notebookutils.credentials.getSecret(keyVaultUrl, secretName) | Scope → Key Vault URL; key → secret name |
dbutils.notebook.run(path, timeout, args) | notebookutils.notebook.run(name, timeout, args) | path → notebook name (relative to workspace) |
dbutils.notebook.exit(value) | notebookutils.notebook.exit(value) | Direct replacement |
dbutils.widgets.get(name) | See § Widgets Migration | No direct equivalent |
dbutils.library.install(...) | Not available at runtime — use Fabric Environments | dbutils.library.restartPython() → notebookutils.session.restartPython() |
dbutils.data.summarize(df) | display(df.summary()) | Use display() or pandas describe() |
Widgets Migration
dbutils.widgets has no direct equivalent in Fabric. Use these patterns instead:
| Use Case | Fabric Pattern |
|---|---|
| Pass parameter from parent notebook | Mark a cell in the child notebook as a parameters cell (notebook UI: cell "..." menu → "Mark cell as parameters"). The parent calls notebookutils.notebook.run("child", arguments={"param": "value"}) — at runtime the engine inserts a new cell beneath the parameters cell that overrides the defaults |
| Pipeline-driven parameterization | Same parameters-cell mechanism; the Fabric Pipeline notebook activity supplies override values via its Base parameters setting |
| Centralized cross-notebook config | Use notebookutils.variableLibrary.getLibrary("<name>") to read values from a Variable Library item (deployment pipelines activate the right value set per stage) |
| Interactive selection in notebook | Use display() with input cells, IPython widgets (Python only), or Fabric Data Activator |
Note:
notebookutils.runtime.contextdoes not expose parameter values. It's for execution metadata (workspace/notebook/activity/user IDs, pipeline-vs-interactive flags, etc.). See dbutils-to-notebookutils.md § Runtime Context.
Cluster Config → Fabric Spark Pools
| Databricks Cluster Concept | Fabric Spark Equivalent | Notes |
|---|---|---|
| All-purpose cluster (interactive) | Starter Pool | Auto-provisioned; no config; ideal for notebooks |
| Job cluster (single-use for jobs) | Custom Pool (or Starter Pool) attached to SJD | Configure node size, autoscale in Fabric capacity settings |
Node type (e.g., Standard_DS3_v2) | Fabric node size (Small/Medium/Large/X-Large/XX-Large) | Map by vCore/memory ratio |
| Autoscale min/max workers | Custom Pool min/max node settings | Available in workspace Spark settings |
spark.conf in cluster settings | Fabric Environment Spark properties | Move to Environment item; attach to workspace or notebook |
init_scripts (cluster init) | Fabric Environment install script | Not fully equivalent — only library installs are supported |
| Databricks Runtime version | Fabric Runtime (1.1 = Spark 3.3, 1.2 = Spark 3.4, 1.3 = Spark 3.5) | Choose matching Spark version; test deprecated APIs |
| Photon accelerator | Fabric Native Execution Engine (NEE) | Enable in workspace Spark settings; vectorized execution similar to Photon |
Databricks Jobs → Spark Job Definitions
| Databricks Jobs Concept | Fabric SJD Equivalent | Notes |
|---|---|---|
| Job with single notebook task | SJD referencing a notebook | Attach a default Lakehouse; pass parameters via SJD args |
| Multi-task job (DAG of tasks) | Fabric Data Pipeline orchestrating multiple SJDs/notebooks | Pipeline activities map to job tasks; dependencies = activity dependencies |
| Job schedule (cron) | Pipeline schedule trigger | Cron expression → recurrence trigger in pipeline |
| Job parameters | SJD default arguments or notebook cell parameters | Parameters cell in notebook is injected at runtime |
| Job clusters per task | Pool attached to SJD | Each SJD can specify its Spark pool independently |
| Databricks Workflows | Fabric Data Pipelines | Full DAG orchestration with conditions, loops, and failure branches |
Delegate to
spark-clifor SJD creation and notebook deployment.
Delta Sharing → Fabric External Data Sharing and OneLake Shortcuts
| Databricks Delta Sharing Pattern | Fabric Equivalent |
|---|---|
| Provider publishes a Delta share | Fabric external data sharing for cross-tenant Fabric data, or a OneLake Shortcut to ADLS Gen2 where the Delta data resides |
| Recipient reads shared data | Accept the external data share into a Lakehouse (Fabric creates a read-only OneLake Shortcut), or create a direct OneLake Shortcut to accessible ADLS Gen2 data |
| Cross-workspace table sharing within org | OneLake Shortcuts pointing to another workspace's Lakehouse tables — no data copy |
| Cross-tenant sharing | Fabric external data sharing — live, read-only, in-place access through a shortcut in the recipient tenant |
When producing a migration workload map, include both paths: direct OneLake Shortcuts for accessible ADLS or same-tenant OneLake data, and Fabric external data sharing for native cross-tenant recipient sharing.
MLflow → Fabric ML Experiments
Fabric ML Experiments are built on the MLflow SDK — most code is directly portable:
| Databricks MLflow Pattern | Fabric Equivalent | Migration Action |
|---|---|---|
mlflow.set_tracking_uri("databricks") | Remove — Fabric tracking is automatic | Delete this line in Fabric notebooks |
mlflow.set_experiment("/path/exp") | mlflow.set_experiment("experiment_name") | Use name only (not path); Fabric creates the Experiment item |
mlflow.log_metric(...) | mlflow.log_metric(...) — identical | No change |
mlflow.log_artifact(...) | mlflow.log_artifact(...) — identical | No change |
mlflow.autolog() | mlflow.autolog() — identical | No change |
mlflow.register_model(...) | mlflow.register_model(...) — identical | Model Registry is available in Fabric ML |
| Databricks Model Serving | Azure ML Online Endpoints or Fabric Data Activator | No direct Fabric model serving yet — use Azure ML |
Must / Prefer / Avoid
MUST DO
- Inventory before prescribing a workspace topology — for workspace-wide requests that omit workload inventory, dependencies, security requirements, data locations, or runtime constraints, ask focused clarifying questions and present conditional choices before selecting a Fabric design
- Replace all
dbutils.*calls using the mapping in dbutils-to-notebookutils.md —dbutilsis not available in Fabric notebooks - Migrate
dbutils.fs.mount()tonotebookutils.fs.mount()(✅ supported — Microsoft Entra default, oraccountKey/sasTokenfrom Key Vault). For cross-workspace or persistent sharing, prefer OneLake Shortcuts instead. Always pairmount()withunmount()intry/finally— Fabric mounts are not released automatically on session end - Replace
dbutils.secrets.get(scope, key)withnotebookutils.credentials.getSecret(keyVaultUrl, secretName)— secret scopes map to Azure Key Vault URLs - Redesign widget-based parameter passing using notebook parameters cells (cell "..." menu → "Mark cell as parameters"); use
notebookutils.variableLibraryfor centralized cross-notebook config.notebookutils.runtime.contextdoes not expose parameter values - Replace
dbutils.library.install*()with Fabric Environments — runtime library installs are not supported in production.dbutils.library.restartPython()maps tonotebookutils.session.restartPython()(Python / PySpark only) - Map Unity Catalog namespaces deliberately — default to one schema-enabled Lakehouse per catalog so
schema.tableis preserved; require a user-approved naming policy only when consolidating multiple catalogs into one Lakehouse. See catalog-migration.md - Map Databricks cluster init scripts to Fabric Environments — cluster-level library installs must move to Environment items
PREFER
- Fabric Native Execution Engine (NEE) as the Photon equivalent — enable in workspace Spark settings for vectorized execution on Delta Lake
- OneLake Shortcuts over data copy for Delta tables that already exist in ADLS Gen2 — point directly without re-ingesting
- Fabric Git Integration as the replacement for Databricks Repos — connect workspace to ADO or GitHub for notebook version control
- Fabric ML Experiments for direct MLflow continuity — tracking code requires minimal changes (remove
set_tracking_uri) - Medallion architecture when restructuring migrated Databricks catalogs — align
bronze,silver,goldUnity Catalog schemas to separate Fabric Lakehouses - Starter Pool for migrating interactive notebook workflows — eliminates cluster startup time that was a common pain point in Databricks job clusters
AVOID
- Do not prescribe a one-size-fits-all workspace topology when the source inventory and migration constraints are missing
- Do not import
dbutilsor attemptdbutils = ...assignments in Fabric notebooks — import attempts fail withModuleNotFoundError, while unresolveddbutilsreferences raiseNameError; always usenotebookutils - Do not retain
dbutils.*calls behind runtime-detection guards (try/except,if IS_DATABRICKS) — replace the calls and Databricks paths outright withnotebookutilsand Fabric paths - Do not assume Unity Catalog governance policies transfer automatically — RBAC, row-level security, and column masking must be reconfigured in Fabric using workspace roles and Lakehouse permissions
- Do not use
%pip installin production Fabric notebooks at runtime — use Fabric Environments for stable, versioned library management - Do not attempt to port Delta Live Tables (DLT) pipelines verbatim — DLT has no Fabric equivalent; rewrite as parameterized notebooks orchestrated by Fabric Pipelines
- Do not rely on Databricks-specific Spark configurations (e.g.,
spark.databricks.*) — these are proprietary and will be silently ignored or raise errors in Fabric - Do not use DBFS paths (
dbfs:/...) — there is no DBFS in Fabric; all paths must use OneLakeabfss://or Lakehouse-relative paths
Multi-Notebook Migration Protocol
For workspaces with >3 notebooks or individual notebooks >5KB, process one notebook at a time (enumerate → export → transform → summarize → deploy → release) to avoid context overflow. Track each notebook through a status lifecycle (inventory → analyzed → converted → deployed → validated, or failed).
Full protocol, status definitions, and per-notebook summary schema: migration-orchestrator.md § Phase 2.
Failure Reporting
When migration cannot complete (permission failures, unresolvable Blockers such as DLT or OS-level init scripts, namespace collisions with no user-chosen policy, or repeated API failures), emit a structured failure report — do not abandon silently. The report captures phase_reached, blockers[] (item / pattern / reason / recommendation), partial_success counts, and next_steps.
Full report schema and stopping conditions: migration-orchestrator.md § Failure Reporting.
Examples
See dbutils-to-notebookutils.md and code-patterns.md for the full mapping. Key quick references:
dbutils.fs → notebookutils.fs
python# Databricks dbutils.fs.ls("/mnt/bronze/orders/") dbutils.fs.cp("/mnt/raw/file.csv", "/mnt/archive/file.csv") # Fabric (replace DBFS/mount paths with OneLake relative paths) notebookutils.fs.ls("Files/bronze/orders/") notebookutils.fs.cp("Files/raw/file.csv", "Files/archive/file.csv")
dbutils.secrets → notebookutils.credentials
python# Databricks pwd = dbutils.secrets.get(scope="prod", key="db-password") # Fabric (scope → Key Vault URL, key → secret name) pwd = notebookutils.credentials.getSecret("https://myvault.vault.azure.net/", "db-password")
Unity Catalog namespace → Lakehouse schema
python# Databricks df = spark.read.table("prod.silver.customers") # Fabric (ProdLakehouse represents the source catalog and is attached as context) df = spark.read.table("silver.customers")
