Databricks 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. dbutils.widgets has no direct equivalent in Fabric — use notebook parameters (cell tag parameters) or notebookutils.runtime.context for context injection
4. dbutils.library (runtime library install) has no equivalent — use Fabric Environments for reproducible library management
5. Unity Catalog uses a 3-level namespace (catalog.schema.table); Fabric Lakehouse uses 2-level (schema.table within a named Lakehouse)

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-authoring-cli. For Fabric Warehouse DDL/DML authoring, see sqldw-authoring-cli.

Table of Contents

Migration Workload Map

dbutils → notebookutils Quick Reference

The complete side-by-side API table is in dbutils-to-notebookutils.md. The key mappings are:

Widgets Migration

dbutils.widgets has no direct equivalent in Fabric. Use these patterns instead:

Cluster Config → Fabric Spark Pools

Databricks Jobs → Spark Job Definitions

Delegate to spark-authoring-cli for SJD creation and notebook deployment.

Delta Sharing → OneLake Shortcuts

MLflow → Fabric ML Experiments

Fabric ML Experiments are built on the MLflow SDK — most code is directly portable:

Must / Prefer / Avoid

MUST DO

• Replace all dbutils.* calls using the mapping in dbutils-to-notebookutils.md — dbutils is not available in Fabric notebooks
• Replace dbutils.fs.mount() with OneLake Shortcuts — Fabric uses token-based identity access; FUSE mounts are not supported
• Replace dbutils.secrets.get(scope, key) with notebookutils.credentials.getSecret(keyVaultUrl, secretName) — secret scopes map to Azure Key Vault URLs
• Redesign widget-based parameter passing using notebook parameter cells (tagged parameters) or notebookutils.runtime.context
• Replace dbutils.library.install() with Fabric Environments — runtime library installs are not supported in production workloads
• Adapt Unity Catalog 3-level namespaces (catalog.schema.table) to Fabric 2-level (schema.table within a 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, gold Unity 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 import dbutils or attempt dbutils = ... assignments in Fabric notebooks — this will raise NameError; always use notebookutils
• 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 install in 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 OneLake abfss:// or Lakehouse-relative paths

Examples

See dbutils-to-notebookutils.md and code-patterns.md for the full mapping. Key quick references:

dbutils.fs → notebookutils.fs

# 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

# 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

# Databricks
df = spark.read.table("prod.silver.customers")

# Fabric (catalog dropped; Lakehouse context provides it)
df = spark.read.table("silver.customers")