Merge pull request #53834 from MicrosoftDocs/NEW-fabric-modeling

3 new Fabric modeling modules

Author: v-dirichards

learn-pr/wwl/choose-data-store-fabric/1-introduction.yml

@@ -0,0 +1,13 @@
+### YamlMime:ModuleUnit
+uid: learn.wwl.choose-data-store-fabric.introduction
+title: Introduction
+metadata:
+  title: Introduction
+  description: "Introduction to choosing analytical data stores in Microsoft Fabric, including lakehouse, warehouse, and eventhouse options."
+  ms.date: 02/23/2026
+  author: angierudduck
+  ms.author: anrudduc
+  ms.topic: unit
+durationInMinutes: 2
+content: |
+  [!include[](includes/1-introduction.md)]

learn-pr/wwl/choose-data-store-fabric/2-describe-options.yml

@@ -0,0 +1,13 @@
+### YamlMime:ModuleUnit
+uid: learn.wwl.choose-data-store-fabric.describe-options
+title: Describe analytical data store options
+metadata:
+  title: Describe Analytical Data Store Options
+  description: "Compare the three primary analytical data stores in Microsoft Fabric: lakehouse, warehouse, and eventhouse. Understand decision factors including data format, query language, and write patterns."
+  ms.date: 02/23/2026
+  author: angierudduck
+  ms.author: anrudduc
+  ms.topic: unit
+durationInMinutes: 6
+content: |
+  [!include[](includes/2-describe-options.md)]

learn-pr/wwl/choose-data-store-fabric/3-evaluate-lakehouse.yml

@@ -0,0 +1,32 @@
+### YamlMime:ModuleUnit
+uid: learn.wwl.choose-data-store-fabric.evaluate-lakehouse
+title: Evaluate lakehouse capabilities
+metadata:
+  title: Evaluate Lakehouse Capabilities
+  description: "Evaluate when to choose a lakehouse in Microsoft Fabric. Understand lakehouse architecture, dual Spark and SQL access, schema flexibility, and ideal use cases for data engineering and data science."
+  ms.date: 02/24/2026
+  author: angierudduck
+  ms.author: anrudduc
+  ms.topic: unit
+durationInMinutes: 6
+content: |
+  [!include[](includes/3-evaluate-lakehouse.md)]
+quiz:
+  title: "Check your understanding"
+  questions:
+  - content: "A data science team needs to process 50 TB of mixed structured CSV files and semi-structured JSON web logs using Python notebooks for exploratory analysis and ML model training. Is lakehouse the right choice?"
+    choices:
+    - content: "Yes, lakehouse is ideal for this scenario"
+      isCorrect: true
+      explanation: "Correct. Mixed data formats (CSV and JSON), Python/Spark notebooks for processing, exploratory analysis with evolving schema, and big data scale (50 TB) are all core lakehouse strengths."
+    - content: "No, choose a different store"
+      isCorrect: false
+      explanation: "Incorrect. Lakehouse is purpose-built for exactly this type of data engineering and data science workload."
+  - content: "A finance team needs to build a star schema where dimension tables require daily UPDATE operations via T-SQL to maintain slowly changing dimensions. Is lakehouse the right choice?"
+    choices:
+    - content: "Yes, lakehouse is ideal for this scenario"
+      isCorrect: false
+      explanation: "Incorrect. While lakehouse has a SQL analytics endpoint, it's read-only. You cannot execute UPDATE, DELETE, or MERGE statements via T-SQL against the lakehouse."
+    - content: "No, choose a different store"
+      isCorrect: true
+      explanation: "Correct. The warehouse provides full T-SQL DML support including UPDATE, DELETE, and MERGE operations for maintaining dimensional models with transactional consistency."

learn-pr/wwl/choose-data-store-fabric/4-evaluate-warehouse.yml

@@ -0,0 +1,32 @@
+### YamlMime:ModuleUnit
+uid: learn.wwl.choose-data-store-fabric.evaluate-warehouse
+title: Evaluate warehouse capabilities
+metadata:
+  title: Evaluate Warehouse Capabilities
+  description: "Evaluate when to choose a warehouse in Microsoft Fabric. Understand full T-SQL support, multi-table ACID transactions, schema-on-write governance, and ideal use cases for BI reporting and dimensional modeling."
+  ms.date: 02/24/2026
+  author: angierudduck
+  ms.author: anrudduc
+  ms.topic: unit
+durationInMinutes: 6
+content: |
+  [!include[](includes/4-evaluate-warehouse.md)]
+quiz:
+  title: "Check your understanding"
+  questions:
+  - content: "A BI team with 10+ years of SQL Server experience needs to build dimensional models with daily dimension updates. They need to support complex multi-table joins for Power BI reports. Is warehouse the right choice?"
+    choices:
+    - content: "Yes, warehouse is ideal for this scenario"
+      isCorrect: true
+      explanation: "Correct. SQL Server expertise translates directly to warehouse T-SQL, dimensional models are optimized in warehouse, transactional updates work for dimension maintenance, and Power BI Direct Lake provides optimal performance."
+    - content: "No, choose a different store"
+      isCorrect: false
+      explanation: "Incorrect. Warehouse is purpose-built for structured BI workloads with T-SQL and dimensional modeling."
+  - content: "An analytics team works entirely in T-SQL and needs to query data across multiple sources: two different warehouses and Delta tables from a lakehouse. They don't want to copy or move data. Is warehouse the right choice for their queries?"
+    choices:
+    - content: "Yes, warehouse is ideal for this scenario"
+      isCorrect: true
+      explanation: "Correct. Warehouse cross-database queries support three-part naming to join data across multiple warehouses and lakehouse SQL analytics endpoints without copying data. This is exactly what the team needs."
+    - content: "No, choose a different store"
+      isCorrect: false
+      explanation: "Incorrect. Since the team uses T-SQL and needs to query across warehouses and lakehouses, warehouse's cross-database query capability is ideal."

learn-pr/wwl/choose-data-store-fabric/5-evaluate-eventhouse.yml

@@ -0,0 +1,32 @@
+### YamlMime:ModuleUnit
+uid: learn.wwl.choose-data-store-fabric.evaluate-eventhouse
+title: Evaluate eventhouse capabilities
+metadata:
+  title: Evaluate Eventhouse Capabilities
+  description: "Evaluate when to choose an eventhouse in Microsoft Fabric. Understand streaming ingestion, KQL analytics, time-series optimization, and ideal use cases for IoT telemetry and real-time monitoring."
+  ms.date: 02/24/2026
+  author: angierudduck
+  ms.author: anrudduc
+  ms.topic: unit
+durationInMinutes: 5
+content: |
+  [!include[](includes/5-evaluate-eventhouse.md)]
+quiz:
+  title: "Check your understanding"
+  questions:
+  - content: "A reporting team needs to analyze sales data that's updated once per day in batch. They build star schema dimensional models and write complex T-SQL queries with multi-table joins. Is eventhouse the right choice?"
+    choices:
+    - content: "Yes, eventhouse is ideal for this scenario"
+      isCorrect: false
+      explanation: "Incorrect. Eventhouse is optimized for streaming time-series data with KQL, not batch dimensional modeling with T-SQL joins."
+    - content: "No, choose a different store"
+      isCorrect: true
+      explanation: "Correct. Warehouse is the right choice for batch-updated dimensional models with T-SQL analytics and BI reporting."
+  - content: "A team collects application log data continuously but only analyzes it weekly to identify trends and errors. They're comfortable learning KQL if it provides benefits. Is eventhouse the right choice?"
+    choices:
+    - content: "Yes, eventhouse is ideal for this scenario"
+      isCorrect: true
+      explanation: "Correct. Even though analysis is weekly, continuous log ingestion and log data's time-series nature make eventhouse a strong fit. KQL's text parsing and pattern matching work well for log analysis."
+    - content: "No, choose a different store"
+      isCorrect: false
+      explanation: "Incorrect. Continuous log ingestion with time-series characteristics aligns with eventhouse strengths, even if analysis happens weekly rather than real-time."

learn-pr/wwl/choose-data-store-fabric/6-exercise.yml

@@ -0,0 +1,13 @@
+### YamlMime:ModuleUnit
+uid: learn.wwl.choose-data-store-fabric.exercise
+title: "Case study - Choose data stores for an integrated analytics solution"
+metadata:
+  title: "Case Study - Choose Data Stores for an Integrated Analytics Solution"
+  description: "Explore how lakehouse, warehouse, eventhouse, and other data stores work together in a complete Fabric architecture through a real-world retail scenario."
+  ms.date: 02/24/2026
+  author: angierudduck
+  ms.author: anrudduc
+  ms.topic: unit
+durationInMinutes: 8
+content: |
+  [!include[](includes/6-exercise.md)]

learn-pr/wwl/choose-data-store-fabric/7-knowledge-check.yml

@@ -0,0 +1,69 @@
+### YamlMime:ModuleUnit
+uid: learn.wwl.choose-data-store-fabric.knowledge-check
+title: Module assessment
+metadata:
+  title: Module Assessment
+  description: "Test your understanding of choosing data stores in Microsoft Fabric, including decision criteria for lakehouse, warehouse, and eventhouse."
+  ms.date: 02/23/2026
+  author: angierudduck
+  ms.author: anrudduc
+  ms.topic: unit
+  module_assessment: true
+durationInMinutes: 3
+quiz:
+  questions:
+  - content: "Which analytical data store in Microsoft Fabric provides full multi-table ACID transaction support through T-SQL?"
+    choices:
+    - content: "Lakehouse"
+      isCorrect: false
+      explanation: "Incorrect. The lakehouse supports single-table ACID transactions through Delta Lake, but it doesn't support multi-table transactions through T-SQL. The lakehouse's SQL analytics endpoint is read-only."
+    - content: "Warehouse"
+      isCorrect: true
+      explanation: "Correct. The warehouse provides full multi-table ACID transaction support through T-SQL, including INSERT, UPDATE, DELETE, and MERGE operations across multiple tables."
+    - content: "Eventhouse"
+      isCorrect: false
+      explanation: "Incorrect. The eventhouse is optimized for append-only streaming ingestion and doesn't support transactional UPDATE or DELETE operations."
+  - content: "A data science team needs to explore a mix of structured transaction data and semi-structured web logs using Python notebooks. Which data store is the best fit?"
+    choices:
+    - content: "Lakehouse"
+      isCorrect: true
+      explanation: "Correct. The lakehouse supports both structured and semi-structured data, and provides native Apache Spark access through Python notebooks for data exploration and machine learning."
+    - content: "Warehouse"
+      isCorrect: false
+      explanation: "Incorrect. The warehouse is designed for structured data and T-SQL development. It doesn't provide Spark notebook access for Python-based data exploration."
+    - content: "Eventhouse"
+      isCorrect: false
+      explanation: "Incorrect. The eventhouse is optimized for time-series and streaming data with KQL as the primary query language. It isn't designed for mixed-format data exploration with Python notebooks."
+  - content: "What is the primary query language used by the eventhouse for time-series analytics?"
+    choices:
+    - content: "T-SQL"
+      isCorrect: false
+      explanation: "Incorrect. While the eventhouse supports a subset of T-SQL, KQL (Kusto Query Language) is the primary query language designed specifically for time-series analysis with built-in operators for aggregations, anomaly detection, and pattern matching."
+    - content: "Spark SQL"
+      isCorrect: false
+      explanation: "Incorrect. Spark SQL is the query language used in lakehouse notebooks. The eventhouse uses KQL as its primary query language."
+    - content: "KQL (Kusto Query Language)"
+      isCorrect: true
+      explanation: "Correct. KQL is the primary query language for the eventhouse, purpose-built for time-series analysis with operators for time-window aggregations, anomaly detection, and pattern matching."
+  - content: "An organization needs to build a star schema with dimension tables that require frequent updates. Which data store best supports this requirement?"
+    choices:
+    - content: "Warehouse"
+      isCorrect: true
+      explanation: "Correct. The warehouse provides full T-SQL DML support including UPDATE, DELETE, and MERGE operations with multi-table ACID transactions, which is essential for maintaining slowly changing dimensions in a star schema."
+    - content: "Lakehouse"
+      isCorrect: false
+      explanation: "Incorrect. While the lakehouse supports single-table transactions through Delta Lake, it doesn't provide the full T-SQL DML surface (UPDATE, DELETE, MERGE) through T-SQL. The SQL analytics endpoint is read-only."
+    - content: "Eventhouse"
+      isCorrect: false
+      explanation: "Incorrect. The eventhouse is designed for append-optimized time-series data, not for relational dimensional models that require frequent row-level updates."
+  - content: "Which feature allows data in one Fabric data store to be accessed from another store without copying or moving the data?"
+    choices:
+    - content: "Cross-database queries and shortcuts"
+      isCorrect: true
+      explanation: "Correct. Shortcuts let you reference data in one store from another without duplication, and cross-database queries in the warehouse let you join data from multiple warehouses and lakehouse SQL analytics endpoints using three-part naming."
+    - content: "Data pipelines"
+      isCorrect: false
+      explanation: "Incorrect. Data pipelines move or copy data between sources and destinations. Shortcuts and cross-database queries provide access to data in place without copying it."
+    - content: "Streaming ingestion"
+      isCorrect: false
+      explanation: "Incorrect. Streaming ingestion is a method for loading real-time data into an eventhouse. It doesn't provide cross-store data access without copying."

learn-pr/wwl/choose-data-store-fabric/8-summary.yml

@@ -0,0 +1,13 @@
+### YamlMime:ModuleUnit
+uid: learn.wwl.choose-data-store-fabric.summary
+title: Summary
+metadata:
+  title: Summary
+  description: "Review key concepts for choosing analytical data stores in Microsoft Fabric, including when to use lakehouse, warehouse, and eventhouse for different business scenarios."
+  ms.date: 02/23/2026
+  author: angierudduck
+  ms.author: anrudduc
+  ms.topic: unit
+durationInMinutes: 2
+content: |
+  [!include[](includes/8-summary.md)]

learn-pr/wwl/choose-data-store-fabric/includes/1-introduction.md

@@ -0,0 +1,16 @@
+Microsoft Fabric provides multiple analytical data stores, each designed for different data patterns and workloads. Choosing the right store for your scenario is one of the most important decisions you make when building an analytics solution. The right choice affects how you ingest data, how your team queries it, and how well your solution adapts as requirements evolve.
+
+The three primary analytical data stores in Fabric are the lakehouse, the warehouse, and the eventhouse. Each one stores data in OneLake using an open format, but they differ in query language support, write capabilities, and the types of workloads they handle best.
+
+Suppose you're a data professional at a retail organization that's adopting Microsoft Fabric. Your team needs to build analytics solutions for several business groups. The sales team wants structured reporting with complex joins. The data science team needs to explore a mix of transaction data and web clickstream logs. The operations team monitors IoT sensor data from distribution centers in real time. Each group has different data types, query preferences, and performance requirements.
+
+Your task is to evaluate Fabric's analytical data stores and recommend the right one for each business group. To do that, you need to understand what each store does well, when to use it, and when a different option is a better fit.
+
+In this module, you:
+
+- Survey the three analytical data store options in Microsoft Fabric: lakehouse, warehouse, and eventhouse.
+- Evaluate the strengths and ideal use cases for each data store.
+- Apply a decision framework to match data characteristics and team skills to the right store.
+- Practice choosing the appropriate data store for real-world business scenarios.
+
+By the end of this module, you're able to evaluate lakehouse, warehouse, and eventhouse capabilities and confidently choose the appropriate data store for a given business scenario.