microsoft/hdinsight-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.

---

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) |

---

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 |

---

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;