hdinsight-migration

name

hdinsight-migration

description

Port Azure HDInsight Spark clusters and Hive workloads to Microsoft Fabric. Removes legacy HiveContext and standalone SparkContext constructors, replacing them with the pre-instantiated SparkSession. Converts WASB and ABFS storage paths to OneLake abfss URLs via Shortcuts. Transforms Hive DDL (STORED AS ORC, external tables) to Delta Lake schemas inside Fabric Lakehouse. Maps Oozie workflow actions — spark, hive, shell, sqoop, coordinator — to Fabric Pipeline activities and schedule triggers. Introduces notebookutils for file and credential operations previously handled via subprocess or HDFS client calls. Use when the user wants to: (1) retire an HDInsight cluster and move to Fabric, (2) convert WASB paths or Hive DDL, (3) replace Oozie coordinators with Fabric Pipelines. Triggers: "migrate from hdinsight", "hdi to fabric", "hivecontext sparksession fabric", "wasb to onelake", "hive ddl to delta", "oozie to fabric pipelines", "hive metastore lakehouse", "hdinsight spark 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. HDInsight has no mssparkutils or dbutils equivalent — notebookutils is net-new capability being introduced
4. HiveContext and SQLContext are legacy Spark 1.x/2.x APIs — Fabric uses Spark 3.x SparkSession exclusively
5. 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.

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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

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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://[e-mail]/	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

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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://[e-mail]/path")	spark.read.text("abfss://[e-mail]/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.

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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.

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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)

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Spark Configuration Differences

HDInsight Concept	Fabric Spark Equivalent	Migration Action
spark-defaults.conf (cluster-wide)	Fabric Spark Workspace Settings + Environment item	Move config properties to Environment or use %%configure in notebooks
%%configure magic	%%configure magic — identical	No change needed
YARN queue / resource allocation	Fabric Spark pool node size and autoscale settings	Map queue SLAs to Custom Pool configuration
Ambari service configs (HDFS, YARN tuning)	Not applicable — Fabric manages infrastructure	Remove; focus on application-level Spark configs
HDI Spark version (e.g., Spark 2.4)	Fabric Runtime 1.3 = Spark 3.5 (latest)	Test for deprecated API removals (e.g., HiveContext, RDD-style ML)
Conda environment / bootstrap.sh	Fabric Environment item with custom libraries	Recreate conda/pip dependencies in a Fabric Environment
hive-site.xml (metastore connection)	Not needed — Delta Lake IS the metastore in Fabric	Remove metastore config; use Lakehouse schemas for namespace organization

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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

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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://[e-mail]/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;