My Fabric notebook is slow. How do I debug?

Click 'Spark application' in the ribbon, then open the Spark UI and check the Stages tab. Look specifically at 'Shuffle read size'. If shuffle is greater than 10x your input data size, you likely have a join or aggregation bottleneck. Fix this by filtering early, broadcasting small tables, or repartitioning.

Should I use Python (PySpark) or Spark SQL in Fabric notebooks?

Use PySpark (Python) for complex logic, machine learning, and loops. Use Spark SQL for standard transformations as it is often more readable for SQL developers. You can mix both in a single notebook using magic commands.

How do I cost-optimize Fabric notebooks?

To reduce costs: 1) Partition tables strategically, 2) Broadcast small tables in joins, 3) Use Liquid Clustering for large tables, 4) Schedule pipelines during off-peak hours, and 5) Monitor Capacity Metrics to smooth out utilization spikes.

Transform Data Using Notebooks in Microsoft Fabric

Q: How do I prevent duplicate data when I transform data using notebooks?

Always use Delta MERGE with a deterministic business key. MERGE operations deduplicate data within the source before merging to the target, preventing duplicates even on multiple reruns.

Q: Can I transform data using notebooks across multiple Lakehouses at once?

Yes. While you attach a primary Lakehouse, you can read from any Lakehouse using the ABFSS path. We recommend using OneLake shortcuts for seamless cross-workspace access without moving data.

Q: Is it safe to run OPTIMIZE and VACUUM in production?

OPTIMIZE is safe to run anytime as it compacts files without affecting readers. VACUUM should be scheduled during maintenance windows because it removes old files; running it while long-running queries are active can cause failures.

Complete 2026 guide to transform data using notebooks. Master production patterns, Delta Lake merge operations, performance optimization, and real-world recipes with official Microsoft best practices.

Updated January 2026 20–25 min read Production-Ready 25+ Code Examples

What does it mean to Transform Data Using Notebooks?

To transform data using notebooks in Microsoft Fabric means utilizing an interactive coding environment (supporting PySpark, Spark SQL, and Scala) to clean, aggregate, and restructure data stored in OneLake. Unlike visual dataflows, notebooks offer full programmatic control, enabling complex logic, machine learning integration, and high-performance processing of large-scale datasets (TB+) using distributed Apache Spark clusters.

Why Transform Data Using Notebooks in Fabric

Transform data using notebooks is the gold standard for data engineering in Microsoft Fabric. Unlike Dataflow Gen2 (visual, low-code) or SQL Warehouse (static queries), notebooks provide:

Full Code Control

Write reproducible PySpark, Spark SQL, or Scala transformations with complete version history.

Spark Power

Distribute processing across clusters. Handle MB to TB+ datasets with parallel execution.

Delta Lake Native

Read/write ACID-compliant Delta tables. Merge, upsert, and optimize with ease.

Pipeline Integration

Orchestrate with Data Pipelines. Schedule, parameterize, and monitor at scale.

Transform Data Using Notebooks vs. Alternatives

Tool	Best For	Skill Level	Scale	Cost
Dataflow Gen2	Visual, low-code mapping & joins	Power Query knowledge	Small–Medium (GB)	High (visuals tax)
SQL Warehouse	Static SQL queries, aggregations	SQL expertise	Large (TB+)	Low (optimized)
Spark Notebooks	Complex logic, feature engineering, ML	Python/Scala + Spark	Medium–Very Large (TB+)	Low–Medium (optimizable)

Community Insight: Reddit users report 90% cost savings when migrating from Dataflow Gen2 to notebooks for large-scale transformations. Notebooks are the most cost-efficient way to transform data using notebooks at enterprise scale.

Quick Start: Transform Data Using Notebooks

Prerequisites

Required Fabric Capacity: F2 or higher
Required Workspace Role: Admin or Member
Required Lakehouse: Fabric Lakehouse as destination
Recommended Git Knowledge: For version control

Step 1: Create & Attach Notebook

Open Workspace

Navigate to your Fabric workspace. Ensure you have Admin or Member permissions.

Create Notebook

Click + New Item → Notebook. Choose default language (PySpark recommended).

Attach Lakehouse

In ribbon: Add → Lakehouse. Select your destination (or create new).

Write First Cell

Read a Delta table and inspect schema.

Step 2: Read Delta Tables

# Read from Lakehouse attached table
df = spark.read.format(“delta”).table(“Lakehouse.schema.sales_raw”)# Inspect
df.printSchema()  # Column names & types
df.show(5)        # First 5 rows
print(f”Rows: {df.count()}”)

Step 3: Transform & Clean

from pyspark.sql.functions import col, when, isnan, current_timestamp# Data quality checks
bad_rows = df.filter(
(col(“amount”).isNull()) |
(col(“amount”) < 0)
)# Write errors to table for review
if bad_rows.count() > 0:
bad_rows.write.format(“delta”).mode(“append”).saveAsTable(“Lakehouse.errors.quality_issues”)# Clean data
df_clean = df.filter(
(col(“amount”).isNotNull()) &
(col(“amount”) >= 0)
).dropDuplicates([“order_id”]) \
.withColumn(“processed_date”, current_timestamp())print(f”✅ {df_clean.count()} rows passed validation”)

Step 4: Write Curated Delta Table

# Merge to curated table (idempotent upsert)
from delta.tables import DeltaTabletarget = DeltaTable.forName(spark, “Lakehouse.curated.sales”)
target.alias(“tgt”).merge(
df_clean.alias(“src”),
“tgt.order_id = src.order_id”
).whenMatchedUpdateAll().whenNotMatchedInsertAll().execute()print(f”✅ Merge complete: {df_clean.count()} rows”)

First Success: You’ve written your first transform data using notebooks pipeline! Now scale it: add error handling, schedule via pipelines, and optimize performance.

5 Production Patterns to Transform Data Using Notebooks

Battle-tested patterns for reliable, production-safe data transformations.

Pattern 1: Schema Enforcement & Data Quality Gates

Intent: Prevent silent failures due to schema drift. Explicit type casting and anomaly capture.

from pyspark.sql.types import StructType, StructField, IntegerType, DoubleType, StringType
from pyspark.sql.functions import col, when, isnan, isnull# Define expected schema upfront
expected_schema = StructType([
StructField(“order_id”, IntegerType(), False),
StructField(“amount”, DoubleType(), False),
StructField(“product”, StringType(), True)
])# Read with schema enforcement
df = spark.read.schema(expected_schema).option(“mode”, “FAILFAST”).csv(“/path/to/input”)# Quality gates
bad_rows = df.filter(
(col(“amount”).isNull()) |
(col(“amount”) < 0) |
(isnan(col("amount")))
)if bad_rows.count() > 0:
bad_rows.withColumn(
“error_reason”,
when(col(“amount”).isNull(), “null_amount”)
.when(col(“amount”) < 0, "negative_amount")
.otherwise("nan_amount")
).write.format("delta").mode("append").saveAsTable("Lakehouse.errors.quality_issues")df_clean = df.filter((col("amount").isNotNull()) & (col("amount") >= 0))
print(f”✅ {df_clean.count()} rows passed quality checks”)

Pattern 2: Idempotent Merges with Delta MERGE

Intent: Make notebooks safe to rerun without duplication. Delta MERGE ensures atomicity.

from delta.tables import DeltaTable
from pyspark.sql.functions import col, current_timestamp# Staged incremental data
staged = spark.read.format(“delta”).table(“Lakehouse.staging.sales_incremental”)target = DeltaTable.forName(spark, “Lakehouse.curated.sales”)# MERGE: update if exists, insert if new
merge_result = (target.alias(“tgt”)
.merge(staged.alias(“src”), “tgt.order_id = src.order_id”)
.whenMatchedUpdateAll()
.whenNotMatchedInsertAll()
.execute()
)print(f”Merge completed: {merge_result} rows affected”)# Advanced: CDC with timestamp checks (only update if source is newer)
merge_cdc = (target.alias(“tgt”)
.merge(staged.alias(“src”), “tgt.order_id = src.order_id”)
.whenMatchedUpdate(
condition=”src.modified_date > tgt.modified_date”,
set={“tgt.amount”: “src.amount”, “tgt.status”: “src.status”}
)
.whenNotMatchedInsertAll()
.execute()
)

Pattern 3: Window Functions for Time-Series Features

from pyspark.sql.window import Window
from pyspark.sql.functions import avg, sum, row_number, lag, col# Rolling 7-day average
sales_window = Window.partitionBy(“product”).orderBy(“date”).rowsBetween(-6, 0)
df_features = df.withColumn(
“rolling_avg_7day”,
avg(“sales”).over(sales_window)
)# Year-over-year comparison
yoy_window = Window.partitionBy(“product”).orderBy(“date”)
df_features = df_features.withColumn(
“prior_year_sales”,
lag(“sales”, 365).over(yoy_window)
)# Ranking products by sales
rank_window = Window.partitionBy(“date”).orderBy(col(“sales”).desc())
df_features = df_features.withColumn(
“product_rank”,
row_number().over(rank_window)
)df_features.display()

Pattern 4: Feature Engineering & Versioning

from pyspark.sql.functions import sum, avg, count, max, datediff, current_date, when, lit# Customer-level aggregations
features_v2 = transactions.groupBy(“customer_id”).agg(
sum(“amount”).alias(“total_spend”),
avg(“amount”).alias(“avg_transaction”),
count(“*”).alias(“transaction_count”),
max(“date”).alias(“last_purchase_date”)
).withColumn(
“days_since_purchase”,
datediff(current_date(), col(“last_purchase_date”))
).withColumn(
“is_high_value”,
when(col(“total_spend”) > 10000, 1).otherwise(0)
).withColumn(
“feature_version”,
lit(“v2”)
)# Write with partition for incremental refresh
features_v2.write.format(“delta”).mode(“overwrite”) \
.option(“partitionBy”, “feature_date”) \
.option(“optimizeWrite”, “true”) \
.saveAsTable(“Lakehouse.features.customer_features_v2″)print(f”✅ Feature table: {features_v2.count()} customers, v2”)

Pattern 5: Incremental Ingestion with Partition Pruning

from datetime import datetime, timedelta
from pyspark.sql.functions import current_date# Process only yesterday’s data
run_date = datetime.now() – timedelta(days=1)
source_path = f”/mnt/raw/sales/{run_date.date()}/”df_incremental = spark.read.format(“csv”).option(“header”, “true”).load(source_path)# Dedupe & normalize
df_clean = df_incremental.dropDuplicates([“order_id”]) \
.withColumn(“ingestion_date”, current_date())# Idempotent merge
target = DeltaTable.forName(spark, “Lakehouse.curated.sales”)
target.alias(“tgt”).merge(
df_clean.alias(“src”),
“tgt.order_id = src.order_id”
).whenMatchedUpdateAll().whenNotMatchedInsertAll().execute()print(f”✅ Incremental load: {run_date.date()}”)

Transform Data Using Notebooks: Performance Optimization

When you transform data using notebooks at scale, optimize for speed and cost. Focus on reducing data movement (shuffle).

Top 5 Performance Levers

1. Partition Strategically

Split tables into 0.5–2 GB partitions by date or business keys. Massive read speedup for range filters.

2. Filter Early

Push predicates before joins. Predicate pushdown reduces data scanned by 50–99%.

3. Broadcast Dimensions

Broadcast small tables (<100 MB) to avoid shuffle in joins. Dramatic speed improvement.

4. Z-Order / Liquid Cluster

Multi-dimensional clustering for targeted queries. 50–80% read time reduction for common filters.

5. Monitor Spark UI

Profile shuffle, task count, and duration. Identify bottlenecks and tune accordingly.

Detailed: Partitioning for Transform Data Using Notebooks

# Bad: no partitioning (single large file, slow reads)
df.write.format(“delta”).mode(“overwrite”).saveAsTable(“table”)# Good: single partition by date
df.write.format(“delta”).mode(“overwrite”) \
.option(“partitionBy”, “date”) \
.saveAsTable(“table”)# Better: multi-level + optimize
df.write.format(“delta”).mode(“overwrite”) \
.option(“partitionBy”, “year,month”) \
.option(“optimizeWrite”, “true”) \
.saveAsTable(“table”)# Best: Liquid Clustering (auto-adaptive, Spark 3.5+)
df.write.format(“delta”).mode(“overwrite”) \
.option(“clusteringColumns”, “customer_id,date”) \
.option(“optimizeWrite”, “true”) \
.saveAsTable(“table”)# Rule of thumb: Each partition should be 0.5–2 GB
# Too small → many small files (slow reads)
# Too large → poor parallelism

Broadcast Joins for Transform Data Using Notebooks

from pyspark.sql.functions import broadcast# Bad: large shuffle if dim table is big
result = sales_df.join(dim_product_df, “product_id”)# Good: broadcast small dimension to all workers
result = sales_df.join(broadcast(dim_product_df), “product_id”)# Rule: If one table < 100 MB and other > 10 GB, ALWAYS broadcast small one
# Can reduce shuffle by 10x or more

Monitor with Spark UI

After running a cell, click Spark application in ribbon → open Spark UI:

Stages tab: Which stage took longest? Look for shuffle indicators.
Shuffle read/write: If shuffle > 10× input data size, investigate join/agg.
Tasks: How many ran in parallel? More = better parallelism.

Watch Out: Shuffle > 50GB for a 10GB input? Likely cartesian product or data skew. Add more partitions or pre-filter to fix.

Notebook UI/UX Best Practices When Transform Data Using Notebooks

Production-grade notebooks are readable, maintainable, and collaborative. Apply these patterns:

1. Header Block with Full Documentation

# ============================================================================
# NOTEBOOK: sales_transform_prod
# PURPOSE: Ingest daily sales CSV, dedupe, validate, merge to curated
# INPUTS:
#   – Raw: /mnt/raw/sales/{run_date}/*.csv
#   – Target: Lakehouse.curated.sales
# OUTPUTS:
#   – Lakehouse.curated.sales (upserted)
#   – Lakehouse.errors.sales_quality (anomalies)
# PARAMETERS: run_date (default: yesterday), environment (dev/test/prod)
# SCHEDULED: Daily @ 2 AM UTC via Data Pipeline “Daily Sales ETL”
# OWNER: data-engineering@company.com
# LAST UPDATED: 2025-01-20
# ============================================================================from datetime import datetime, timedelta
from pyspark.sql.functions import *print(f”🚀 Starting: transform data using notebooks pipeline”)

2. Centralized Configuration Cell

# ============================================================================
# CONFIGURATION
# ============================================================================RUN_DATE = dbutils.widgets.get(“run_date”, str((datetime.now() – timedelta(days=1)).date()))
ENVIRONMENT = dbutils.widgets.get(“environment”, “prod”)if ENVIRONMENT == “prod”:
SOURCE_PATH = “abfss://workspace@prodstorage.dfs.core.windows.net/raw/sales”
CURATED_TABLE = “Lakehouse.curated.sales”
else:
SOURCE_PATH = “abfss://workspace@devstorage.dfs.core.windows.net/raw/sales”
CURATED_TABLE = “Lakehouse_Dev.curated.sales”spark.conf.set(“spark.sql.shuffle.partitions”, “200”)
spark.conf.set(“spark.databricks.delta.optimizeWrite.enabled”, “true”)print(f”Config: RUN_DATE={RUN_DATE}, ENV={ENVIRONMENT}, TARGET={CURATED_TABLE}”)

3. Rich Visualizations & Interactivity

# Use display() instead of show() for rich HTML output
result_df.display()  # Rich table with filtering, sorting# Matplotlib plotting
import matplotlib.pyplot as plt
result_df.toPandas().plot(kind=’bar’, figsize=(10,6))
plt.title(“Sales by Region”)
plt.show()

4. Error Handling & Logging

import logginglogger = logging.getLogger(“sales_transform”)
logger.setLevel(logging.INFO)try:
print(“📥 Loading data…”)
df_raw = spark.read.option(“header”, “true”).csv(f”{SOURCE_PATH}/{RUN_DATE}/”)
logger.info(f”Loaded {df_raw.count()} rows”)
except Exception as e:
logger.error(f”❌ Load failed: {str(e)}”)
raise

Orchestration: Transform Data Using Notebooks at Scale

Interactive development is great; production reliability requires Data Pipelines and CI/CD.

Step 1: Parameterize Your Notebook

# Define parameters users can override at runtime
dbutils.widgets.text(“run_date”, str((datetime.now() – timedelta(days=1)).date()), “Run Date”)
dbutils.widgets.dropdown(“environment”, “prod”, [“dev”, “test”, “prod”], “Environment”)RUN_DATE = dbutils.widgets.get(“run_date”)
ENVIRONMENT = dbutils.widgets.get(“environment”)

Step 2: Add to Data Pipeline

Create new Data Pipeline in workspace.
Drag Notebook activity onto canvas.
In Settings, select your notebook and pass parameters via pipeline variables.
Add downstream steps (copy, other notebooks) to build workflow.

Step 3: Schedule & Monitor

Click Schedule in pipeline ribbon.
Set recurrence (daily, hourly, weekly).
Configure alerts (email on failure, retry policy, timeout).
Monitor from Monitor hub → Capacity metrics.

Step 4: CI/CD & Version Control

Store notebooks in Git (Azure Repos, GitHub) and use Fabric CI/CD best practices:

# Notebook structure in Git
data-fabric-project/
├── notebooks/
│   ├── sales_transform.py
│   ├── customer_features.py
│   └── data_quality_checks.py
├── tests/
│   ├── test_sales_transform.py
├── configs/
│   └── pipeline_params.json
└── README.md

Production Readiness: Parameterized notebooks + pipelines + Git = reliable, auditable data engineering. This is the Fabric standard for enterprise deployments.

Security & Governance When Transform Data Using Notebooks

Three-Layer RBAC in Fabric

Layer	Control	Example
Workspace	Admin/Member/Viewer roles	Only engineers are Members. Analysts are Viewers.
Lakehouse	Row-level security (RLS) + column masking	NA managers see only NA data. SSNs are masked.
Execution	Service principal / user identity	Notebook runs under your Entra ID; inherits Lakehouse permissions.

Data Masking for PII

from pyspark.sql.functions import col, when, substring, hash# Mask SSN: show only last 4 digits
df_masked = df.withColumn(
“ssn_masked”,
when(col(“ssn”).isNotNull(), concat(lit(“XXX-XX-“), substring(col(“ssn”), 8, 4)))
.otherwise(None)
).drop(“ssn”)# Hash email for anonymization
df_masked = df_masked.withColumn(
“email_hash”,
hash(col(“email”))
).drop(“email”)# Verify no PII remains
assert len([c for c in df_masked.columns if c in [“ssn”, “email”, “phone”]]) == 0, “PII leaked!”

Audit Logging & Lineage

Microsoft Purview integrates with Fabric to track:

Who accessed what data and when
What transformations ran
Data lineage (notebook → Delta table)

Enable Purview integration and automatically capture lineage for compliance audits.

Real-World Recipes: Transform Data Using Notebooks

Recipe 1: Daily Incremental Sales Load + Quality Checks

Scenario: CSV files arrive daily. Load, dedupe, validate, and upsert to curated Delta table.

from datetime import datetime, timedelta
from pyspark.sql.functions import *
from delta.tables import DeltaTableRUN_DATE = datetime.now() – timedelta(days=1)
SOURCE_PATH = f”abfss://raw/sales/{RUN_DATE.date()}/”
CURATED_TABLE = “Lakehouse.curated.sales”print(f”🚀 Daily sales load: {RUN_DATE.date()}”)# 1. READ
df_raw = spark.read.option(“header”, “true”).csv(SOURCE_PATH)
print(f”Loaded {df_raw.count()} raw rows”)# 2. QUALITY CHECKS
bad_rows = df_raw.filter((col(“order_id”).isNull()) | (col(“amount”) < 0))
if bad_rows.count() > 0:
bad_rows.write.format(“delta”).mode(“append”).saveAsTable(“Lakehouse.errors.sales_quality”)# 3. CLEAN & ENRICH
df_clean = df_raw.filter(
(col(“order_id”).isNotNull()) & (col(“amount”) >= 0)
).dropDuplicates([“order_id”]) \
.withColumn(“ingestion_date”, lit(RUN_DATE.date())) \
.withColumn(“ingestion_ts”, current_timestamp())# 4. MERGE (idempotent)
target = DeltaTable.forName(spark, CURATED_TABLE)
target.alias(“tgt”).merge(
df_clean.alias(“src”),
“tgt.order_id = src.order_id”
).whenMatchedUpdateAll().whenNotMatchedInsertAll().execute()print(f”✅ Merged {df_clean.count()} rows for {RUN_DATE.date()}”)

Recipe 2: Customer Feature Engineering for ML

Scenario: Build versioned feature table from transaction history for ML training.

from pyspark.sql.functions import *
from pyspark.sql.window import Window# Load transaction data
transactions = spark.read.format(“delta”).table(“Lakehouse.curated.sales”)# 1. AGGREGATIONS
customer_agg = transactions.groupBy(“customer_id”).agg(
count(“*”).alias(“transaction_count”),
sum(“amount”).alias(“total_spend”),
avg(“amount”).alias(“avg_transaction”),
max(“date”).alias(“last_purchase_date”)
)# 2. TEMPORAL FEATURES
customer_agg = customer_agg.withColumn(
“days_since_purchase”,
datediff(current_date(), col(“last_purchase_date”))
)# 3. FLAGS
customer_agg = customer_agg.withColumn(
“is_high_value”,
when(col(“total_spend”) > 10000, 1).otherwise(0)
)# 4. VERSION & WRITE
customer_agg = customer_agg.withColumn(“feature_version”, lit(“v2”)) \
.withColumn(“feature_date”, current_date())customer_agg.write.format(“delta”).mode(“overwrite”) \
.option(“partitionBy”, “feature_date”) \
.option(“optimizeWrite”, “true”) \
.saveAsTable(“Lakehouse.features.customer_features_v2″)print(f”✅ Saved {customer_agg.count()} customer features”)

Recipe 3: Hourly KPI Aggregation for BI

Scenario: Refresh KPI summary hourly for Power BI dashboards.

from delta.tables import DeltaTable# Load curated sales
sales = spark.read.format(“delta”).table(“Lakehouse.curated.sales”)# Aggregate by region & date
kpi_summary = sales.filter(col(“date”) >= current_date() – 30).groupBy(“date”, “region”).agg(
sum(“amount”).alias(“daily_revenue”),
count(“*”).alias(“daily_orders”),
avg(“amount”).alias(“avg_order_value”)
).withColumn(“report_ts”, current_timestamp())# Merge to KPI table (Power BI queries this)
target_kpi = DeltaTable.forName(spark, “Lakehouse.reporting.kpi_daily”)
target_kpi.alias(“tgt”).merge(
kpi_summary.alias(“src”),
“tgt.date = src.date AND tgt.region = src.region”
).whenMatchedUpdateAll().whenNotMatchedInsertAll().execute()print(f”✅ Updated {kpi_summary.count()} KPI rows”)

Comparison: When to Use Notebooks vs. Alternatives

When should you transform data using notebooks vs. Dataflow, Warehouse, or Pipelines?

🔷 Notebooks (This Guide)

Iterative, complex transformations
Feature engineering & ML preprocessing
Multi-step CDC pipelines
Custom business logic
90% cheaper than Dataflows for large scale
Production-grade reliability

📊 Dataflow Gen2

Visual, drag-and-drop interface
Power Query familiar to BI users
Good for simple mapping/joins
Limited complex logic
Higher cost per GB processed
Best for exploratory projects

🏢 SQL Warehouse

Static, declarative SQL queries
Large-scale aggregations
Best for structured business data
T-SQL / stored procs
Optimized for BI queries
Often more cost-efficient than notebooks for SQL-only workloads

⚙️ Data Pipelines

Orchestration & scheduling
Copy activity for data movement
Chains notebooks & other activities
Not suitable for standalone transformation
Essential for production reliability
Works alongside notebooks

Decision Rule: Use Notebooks to transform data when: (1) logic is complex, (2) you need parallelism, (3) cost matters (notebooks are cheaper). Use SQL Warehouse for simple aggregations. Use Dataflows for low-code exploration. Use Pipelines to orchestrate everything.

FAQ: Transform Data Using Notebooks

Q1: How do I prevent duplicate data when I transform data using notebooks?

Answer: Always use Delta MERGE with a deterministic business key. MERGE deduplicates within the source before merging to the target, preventing duplicates even on reruns.

Q2: My notebook is slow. How do I debug?

Answer: Click Spark application → Spark UI → Stages tab. Check Shuffle size. If shuffle > 10× input, you have a join/agg bottleneck. Fix by: (1) filtering early, (2) broadcasting small tables, (3) repartitioning.

Q3: Can I transform data using notebooks across multiple Lakehouses at once?

Answer: Yes. Attach primary Lakehouse, but you can read from any Lakehouse using spark.read.format(“delta”).load(“abfss://…”). Use OneLake shortcuts for seamless cross-workspace access.

Q4: Is it safe to run OPTIMIZE and VACUUM in production?

Answer: OPTIMIZE: Safe anytime (compacts files, readers unaffected). VACUUM: Schedule during maintenance windows; avoid while queries run. Removes old files after N days (default 7).

Q5: Should I use Python or Spark SQL in notebooks?

Answer: PySpark (Python): Better for complex logic, ML, pandas integration. Spark SQL: Faster for pure SQL workloads. Mix both in one notebook using `%%sql` magic commands.

Q6: How do I version-control notebooks?

Answer: Store in Git (Azure Repos, GitHub). Use Fabric CI/CD for PR validation. Deploy via deployment pipelines or Terraform.

Q7: Can I use R or Scala when I transform data using notebooks?

Answer: R: Limited support (SparkR). Not recommended. Scala: Supported, but Python is the industry standard. Stick with Python unless you need low-level Spark optimization.

Q8: How do I cost-optimize notebooks?

Answer: (1) Partition tables & filter early, (2) Broadcast small tables, (3) Use Liquid Clustering, (4) Schedule during off-peak, (5) Monitor Capacity Metrics for spikes.

References: Transform Data Using Notebooks

Official Microsoft Documentation

How to Use Microsoft Fabric Notebooks – Official setup & API reference
Spark in Microsoft Fabric – Runtime, compute pools, optimization
Delta Lake Optimization – V-Order, OPTIMIZE, MERGE syntax
Spark Best Practices – Official security, capacity, development guidance
DP-600 Exam Guide – Fabric data engineer certification

Community & External Resources

Delta Lake Documentation – Official format & merge reference
Apache Spark Docs – SQL, RDD, DataFrame APIs
Databricks Academy – Free Spark & Delta courses
MS Learn: Prepare & Transform in Lakehouse – Hands-on module
Reddit: r/MicrosoftFabric – Community Q&A

Living Document: This guide is updated quarterly. Check Microsoft Learn regularly for new features, expanded data source support, and pricing updates.

Ready to Transform Data Using Notebooks?

Start with a single daily ETL notebook. Run via pipeline. Monitor. Iterate. Once confident, scale to multi-source transformations, feature engineering, and ML pipelines.

Notebooks in Fabric are free to develop—you pay only for compute. Iterate rapidly. Build with confidence.

Start with Official Docs Explore Fabric Series