refreshed module

Author: weslbo

learn-pr/wwl-data-ai/introduction-to-data-engineering-azure/1-introduction.yml

@@ -4,7 +4,7 @@ title: Introduction
 metadata:
   title: Introduction
   description: "Introduction"
-  ms.date: 08/21/2025
+  ms.date: 04/03/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: unit

learn-pr/wwl-data-ai/introduction-to-data-engineering-azure/2-what-data-engineering.yml

@@ -4,7 +4,7 @@ title: What is data engineering
 metadata:
   title: What is Data Engineering
   description: "What is data engineering"
-  ms.date: 08/21/2025
+  ms.date: 04/03/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: unit

learn-pr/wwl-data-ai/introduction-to-data-engineering-azure/4-common-patterns-azure-data-engineering.yml

@@ -4,7 +4,7 @@ title: Important data engineering concepts
 metadata:
   title: Important Data Engineering Concepts
   description: "Important data engineering concepts"
-  ms.date: 08/21/2025
+  ms.date: 04/03/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: unit

learn-pr/wwl-data-ai/introduction-to-data-engineering-azure/5-common-tooling-azure-data-engineering.yml

@@ -4,7 +4,7 @@ title: Data engineering in Microsoft Azure
 metadata:
   title: Data Engineering in Microsoft Azure
   description: "Data engineering in Microsoft Azure"
-  ms.date: 08/21/2025
+  ms.date: 04/03/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: unit

learn-pr/wwl-data-ai/introduction-to-data-engineering-azure/6-knowledge-check.yml

@@ -4,7 +4,7 @@ title: Module assessment
 metadata:
   title: Module Assessment
   description: "Knowledge check"
-  ms.date: 08/21/2025
+  ms.date: 04/03/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: unit

learn-pr/wwl-data-ai/introduction-to-data-engineering-azure/7-summary.yml

@@ -4,7 +4,7 @@ title: Summary
 metadata:
   title: Summary
   description: "Summary"
-  ms.date: 08/21/2025
+  ms.date: 04/03/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: unit

learn-pr/wwl-data-ai/introduction-to-data-engineering-azure/includes/1-introduction.md

@@ -1,3 +1 @@
-
-
-In most organizations, a data engineer is the primary role responsible for integrating, transforming, and consolidating data from various structured and unstructured data systems into structures that are suitable for building analytics solutions. An Azure data engineer also helps ensure that data pipelines and data stores are high-performing, efficient, organized, and reliable, given a specific set of business requirements and constraints. 
+In most organizations, a data engineer is the primary role responsible for integrating, transforming, and consolidating data from various structured and unstructured data systems into structures that are suitable for building analytics solutions. An Azure data engineer also helps ensure that data pipelines and data stores are high-performing, efficient, organized, and reliable, given a specific set of business requirements and constraints. 

learn-pr/wwl-data-ai/introduction-to-data-engineering-azure/includes/2-what-data-engineering.md

@@ -1,19 +1,17 @@
-
-
 The data engineer will often work with multiple types of data to perform many operations using many scripting or coding languages that are appropriate to their individual organization.
 
 ## Types of data
 
-There are three primary types of data that a data engineer will work with.
+There are three primary types of data that a data engineer works with.
 
 | Structured | Semi-structured | Unstructured |
-| -- | -- | -- |
+|--|--|--|
 | ![Diagram of Structured data type.](../media/2-structured-data.png) | ![Diagram of Semi-structured data type.](../media/2-semi-structured-data.png) | ![Diagram of Unstructured data type.](../media/2-unstructured-data.png) |
 | Structured data primarily comes from table-based source systems such as a relational database or from a flat file such as a comma separated (CSV) file. The primary element of a structured file is that the rows and columns are aligned consistently throughout the file. | Semi-structured data is data such as JavaScript object notation (JSON) files, which may require flattening prior to loading into your source system. When flattened, this data doesn't have to fit neatly into a table structure. | Unstructured data includes data stored as key-value pairs that don't adhere to standard relational models and Other types of unstructured data that are commonly used include portable data format (PDF), word processor documents, and images. |
 
 ## Data operations
 
-As a data engineer some of the main tasks that you'll perform in Azure include *data integration*, *data transformation*, and *data consolidation*.
+As a data engineer some of the main tasks that you perform in Azure include *data integration*, *data transformation*, and *data consolidation*.
 
 ### Data integration
 
@@ -39,6 +37,8 @@ Data Engineers must be proficient with a range of tools and scripting languages
 
 - **SQL** - One of the most common languages data engineers use is SQL, or Structured Query Language, which is a relatively easy language to learn. SQL uses queries that include SELECT, INSERT, UPDATE, and DELETE statements to directly work with the data stored in tables.
 
-- **Python** - Python is one of the most popular and fastest growing programming languages in the world. It's used for all sorts of tasks, including web programming and data analysis. It has emerged as the language to learn for machine learning, and is increasing in popularity in data engineering with the use of notebooks.
+- **Python** - Python is one of the most popular and fastest growing programming languages in the world. It's used for all sorts of tasks, including web programming and data analysis. It has emerged as the language to learn for machine learning, and is increasing in popularity in data engineering with the use of notebooks. In large-scale data engineering workloads, data engineers typically use PySpark—the Python API for Apache Spark—to write transformation logic that runs across distributed Spark clusters.
+
+- **KQL** - Kusto Query Language (KQL) is a query language for analyzing streaming and log data in real-time analytics scenarios. Data engineers use KQL in Microsoft Fabric's Real-Time Intelligence workload and Azure Data Explorer to query high-velocity data streams.
 
 - **Others** - Depending upon the needs of the organization and your individual skill set, you may also use other popular languages within or outside of notebooks including R, Java, Scala, C#, and more. The use of notebooks is growing in popularity, and allows collaboration using different languages within the same notebook.

learn-pr/wwl-data-ai/introduction-to-data-engineering-azure/includes/4-common-patterns-azure-data-engineering.md

@@ -1,12 +1,10 @@
-
-
 There are some core concepts with which data engineers should be familiar. These concepts underpin many of the workloads that data engineers must implement and support.
 
 ## Operational and analytical data
 
 ![Diagram representing operational and analytical data.](../media/4-operational-analytical-data.png)
 
-*Operational* data is usually transactional data that is generated and stored by applications, often in a relational or non-relational database. *Analytical* data is data that has been optimized for analysis and reporting, often in a data warehouse.
+*Operational* data is usually transactional data that is generated and stored by applications, often in a relational, or non-relational database. *Analytical* data is data that has been optimized for analysis and reporting, often in a data warehouse.
 
 One of the core responsibilities of a data engineer is to design, implement, and manage solutions that integrate operational and analytical data sources or extract operational data from multiple systems, transform it into appropriate structures for analytics, and load it into an analytical data store (usually referred to as ETL solutions).
 
@@ -40,6 +38,12 @@ A data warehouse is a centralized repository of integrated data from one or more
 
 Data engineers are responsible for designing and implementing relational data warehouses, and managing regular data loads into tables.
 
+## Lakehouses
+
+A lakehouse combines the scalability of a data lake with the querying capabilities of a data warehouse. Rather than maintaining two separate systems—one for raw file storage and one for structured analytics—a lakehouse stores all data in a single location using the Delta Lake format, which provides ACID transactions, schema enforcement, and support for both structured and unstructured data.
+
+This means data engineers can use Apache Spark and notebooks to ingest and transform raw data, while analysts can query the same data using familiar SQL tools—without moving data between systems. The lakehouse architecture has become central to modern data engineering on Microsoft Azure, and is the primary data store pattern used in Microsoft Fabric.
+
 ## Apache Spark
 
 ![Diagram representing an Apache Spark cluster.](../media/4-apache-spark.png)

learn-pr/wwl-data-ai/introduction-to-data-engineering-azure/includes/5-common-tooling-azure-data-engineering.md

@@ -1,17 +1,17 @@
-
-
 ![Diagram of the flow of a typical enterprise data analytics solution.](../media/3-data-engineering-azure.png)
 
 Microsoft Azure includes many services that can be used to implement and manage data engineering workloads.
 
 The diagram displays the flow from left to right of a typical enterprise data analytics solution, including some of the key Azure services that may be used. Operational data is generated by applications and devices and stored in Azure data storage services such as Azure SQL Database, Azure Cosmos DB, and Microsoft Dataverse. Streaming data is captured in event broker services such as Azure Event Hubs.
 
-This operational data must be captured, ingested, and consolidated into analytical stores; from where it can be modeled and visualized in reports and dashboards. These tasks represent the core area of responsibility for the data engineer. The core Azure technologies used to implement data engineering workloads include:
+This operational data must be captured, ingested, and consolidated into analytical stores; from where it can be modeled and visualized in reports and dashboards. These tasks represent the core area of responsibility for the data engineer. The core Microsoft technologies used to implement data engineering workloads include:
 
-- Azure Synapse Analytics
+- Microsoft Fabric
 - Azure Data Lake Storage Gen2
 - Azure Stream Analytics
 - Azure Data Factory
 - Azure Databricks
 
+Microsoft Fabric is a unified, end-to-end SaaS analytics platform built on OneLake that brings together data engineering, data factory, data science, data warehousing, real-time intelligence, databases, business intelligence, and IQ in a single integrated environment. IQ is a workload for unifying business semantics across data, models, and systems to power intelligent agents and decisions grounded in a live, holistic view of the business.
+
 The analytical data stores that are populated with data produced by data engineering workloads support data modeling and visualization for reporting and analysis, often using sophisticated visualization tools such as Microsoft Power BI.