hdinsight-migration

by microsoft

Update Check — ONCE PER SESSION (mandatory) The first time this skill is used in a session, run the check-updates skill before proceeding. npx skills add https://github.com/microsoft/skills-for-fabric --skill hdinsight-migration Download ZIPGitHub716

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

• 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

• HDInsight has no mssparkutils or dbutils equivalent — notebookutils is net-new capability being introduced

• HiveContext and SQLContext are legacy Spark 1.x/2.x APIs — Fabric uses Spark 3.x SparkSession exclusively

• wasb:// paths are deprecated and require a Storage Account key or SAS — replace with OneLake shortcuts

HDInsight → 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

Topic Reference Migration Workload Map § Migration Workload Map SparkSession & Context API Changes § SparkSession API Changes WASB / ABFS → OneLake Path Migration path-migration.md Hive DDL → Delta Lake / Lakehouse Schemas hive-to-delta.md Oozie → Fabric Pipelines § Oozie → Fabric Pipelines Introducing notebookutils § Introducing notebookutils Before/After Code Patterns code-patterns.md 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

Migration Workload Map

HDInsight Component Fabric Target Notes Spark cluster (notebooks, scripts) Fabric Spark (Lakehouse / Notebooks / SJD) No persistent cluster — Starter Pool or Custom Pool provides on-demand Spark Hive / HiveServer2 Lakehouse SQL Endpoint + Lakehouse schemas Delta Lake replaces Hive metastore; schemas provide namespace equivalent HBase Fabric Warehouse or Azure Cosmos DB (separate from Fabric) HBase has no direct Fabric equivalent — assess workload access patterns Oozie workflows Fabric Data Pipelines Map Oozie actions to Fabric activities; see § Oozie → Fabric Pipelines YARN Resource Manager Fabric Spark monitoring (Spark UI, Monitoring Hub) No YARN — Fabric manages compute automatically Ambari Fabric Monitoring Hub + Admin Portal Cluster health, capacity, and job monitoring WASB / ABFS storage OneLake Shortcuts → abfss:// [email protected] / See path-migration.md Ranger policies Fabric workspace roles + OneLake data access roles Map Ranger row/column filters to Lakehouse row-level security Livy REST server Fabric Livy API Compatible endpoint — see SPARK-AUTHORING-CORE.md

SparkSession & Context API Changes

HDInsight Spark clusters often use legacy Spark 1.x / 2.x API styles. Replace all of these with the unified SparkSession:

Legacy HDInsight Pattern Fabric Spark 3.x Replacement from pyspark import SparkContext; sc = SparkContext() Not needed — sc = spark.sparkContext (pre-instantiated) from pyspark.sql import HiveContext; hc = HiveContext(sc) Not needed — spark session has Hive-compatible SQL support via Delta schemas from pyspark.sql import SQLContext; sqlc = SQLContext(sc) Not needed — use spark.sql(...) directly SparkSession.builder.enableHiveSupport().getOrCreate() Not needed in Fabric — spark is pre-built and available sc.textFile("wasb:// [email protected] /path") spark.read.text("abfss:// [email protected] /lh.Lakehouse/Files/path") sqlContext.sql("CREATE TABLE ... STORED AS ORC") See hive-to-delta.md for Delta DDL equivalent

In Fabric notebooks, spark (SparkSession) and sc (SparkContext) are pre-instantiated — do not call SparkContext() or SparkSession.builder...getOrCreate() at the top of migrated notebooks.

Oozie → Fabric Pipelines

Map Oozie workflow actions to Fabric Data Pipeline activities:

Oozie Action Type Fabric Pipeline Activity Notes <spark> action Notebook activity or Spark Job Definition activity Pass parameters via notebook cell parameters or SJD arguments <hive> action Script activity (SQL) against Lakehouse SQL Endpoint Convert HiveQL to Spark SQL or Delta SQL <shell> action Azure Function activity or Web activity Shell scripts must be refactored; no direct shell execution in Fabric Pipelines <java> action Azure Batch activity (external) or refactor to PySpark Java MapReduce jobs must be rewritten <sqoop> action Copy Data activity (Fabric Data Factory connector) Sqoop import/export maps to Fabric Copy Data with JDBC source/sink <coordinator> (time-based schedule) Pipeline schedule trigger Set recurrence in pipeline trigger; supports cron-like expressions <coordinator> (data-triggered) Storage Event trigger Trigger on OneLake file arrival

Delegate to spark-authoring-cli for notebook and SJD creation after mapping pipeline activities.

Introducing notebookutils

HDInsight Spark had no built-in utility framework equivalent to mssparkutils or dbutils. When migrating to Fabric, introduce notebookutils for common operations:

Operation Old HDInsight Approach notebookutils Equivalent List files dbutils (N/A) / HDFS CLI notebookutils.fs.ls("abfss://...") Copy file HDFS API / shutil notebookutils.fs.cp(src, dest) Read secret Azure Key Vault REST call notebookutils.credentials.getSecret(keyVaultUrl, secretName) Get notebook context Not available notebookutils.runtime.context — returns workspace ID, notebook ID, etc. Run child notebook Not available notebookutils.notebook.run("notebook_name", timeout, {"param": "value"}) Exit notebook with value sys.exit() notebookutils.notebook.exit("value") Mount storage WASB config in spark-defaults.conf OneLake Shortcut (no runtime mount needed)

Must / Prefer / Avoid

MUST DO

• Replace all wasb:// / wasbs:// paths with OneLake abfss:// paths or OneLake Shortcuts — wasb:// requires storage account keys which are not the Fabric-preferred auth model

• Replace HiveContext, SQLContext, and standalone SparkContext() — use the pre-instantiated spark session in Fabric notebooks

• Migrate Hive DDL (STORED AS ORC, LOCATION, TBLPROPERTIES) to Delta Lake DDL — see hive-to-delta.md

• Introduce notebookutils for file system operations, secret retrieval, and child notebook orchestration where HDInsight used custom scripts or direct API calls

• Replace Oozie XML workflows with Fabric Data Pipelines — see § Oozie → Fabric Pipelines

• Align library management to Fabric Environments — remove bootstrap.sh, conda envs, and runtime %pip install patterns for production workloads

PREFER

• OneLake Shortcuts over copying data — mount existing ADLS Gen2 containers as shortcuts to avoid re-ingestion during migration

• Delta Lake for all tables migrated from Hive ORC/Parquet — ACID guarantees, time travel, and schema enforcement improve data quality

• Fabric Starter Pool for initial migration validation — no pool configuration overhead, fast session startup

• Lakehouse schemas (database namespaces) for organizing migrated Hive databases — one schema per Hive database within a single Lakehouse

• Medallion architecture for restructuring migrated data layers during migration — align Bronze/Silver/Gold with raw Hive → validated Delta → serving Gold patterns

AVOID

• Do not use SparkContext() or HiveContext() constructors in Fabric notebooks — they conflict with the pre-instantiated spark session and will raise errors

• Do not use hive-site.xml or external Hive metastore configuration — Fabric's Delta Lake-backed Lakehouse IS the metastore

• Do not assume YARN queue mappings translate to Fabric pools — re-design resource allocation based on Fabric Spark pool SLAs

• Do not attempt to run Oozie shell actions or Java MapReduce jobs directly in Fabric — these must be refactored (see § Oozie → Fabric Pipelines )

• Do not use %sh magic for file system operations in production notebooks — use notebookutils.fs.* for portability and OneLake token-based auth

Examples

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

Legacy context → Fabric pre-instantiated session

# HDInsight (remove entirely)
from pyspark.sql import HiveContext
hc = HiveContext(sc)

# Fabric — use pre-instantiated spark directly
df = spark.sql("SELECT * FROM sales.fact_orders")

WASB path → OneLake path (after shortcut creation)

# HDInsight
df = spark.read.parquet("wasb://[email protected]/orders/")

# Fabric
df = spark.read.parquet("Files/raw/orders/")

Hive DDL → Delta DDL

-- HDInsight
CREATE TABLE sales_db.fact_orders (...) STORED AS ORC LOCATION 'wasb://...';

-- Fabric
CREATE SCHEMA IF NOT EXISTS sales_db;
CREATE TABLE sales_db.fact_orders (...) USING DELTA;