Merge pull request #52970 from weslbo/final-edits

final edits

Author: Stacyrch140

learn-pr/wwl-databricks/design-implement-data-modeling-unity-catalog/index.yml

@@ -1,15 +1,15 @@
 ### YamlMime:Module
 uid: learn.wwl.design-implement-data-modeling-unity-catalog
 metadata:
-  title: Design and Implement Data Modeling
+  title: Design and Implement Data Modeling with Azure Databricks
   description: Learn how to design and implement data modeling strategies in Azure Databricks with Unity Catalog, including ingestion patterns, table formats, partitioning, slowly changing dimensions, and clustering strategies.
   ms.date: 12/07/2025
   author: weslbo
   ms.author: wedebols
   ms.topic: module
   ms.service: azure-databricks
   ai-usage: ai-generated
-title: Design and implement data modeling
+title: Design and implement data modeling with Azure Databricks
 summary: Effective data modeling forms the foundation of a performant and maintainable data platform. This module explores how to design ingestion logic, select appropriate tools and table formats, implement partitioning schemes, manage slowly changing dimensions, choose appropriate data granularity, and optimize table performance through clustering strategies in Azure Databricks with Unity Catalog.
 abstract: |
   By the end of this module, you'll be able to:

learn-pr/wwl-databricks/design-implement-data-pipelines/index.yml

@@ -1,15 +1,15 @@
 ### YamlMime:Module
 uid: learn.wwl.design-implement-data-pipelines
 metadata:
-  title: Design and Implement Data Pipelines
+  title: Design and Implement Data Pipelines with Azure Databricks
   description: Learn how to design and implement robust data pipelines using Lakehouse architecture principles, medallion architecture, and Lakeflow Declarative Pipelines in Azure Databricks.
   ms.date: 12/07/2025
   author: weslbo
   ms.author: wedebols
   ms.topic: module
   ms.service: azure-databricks
   ai-usage: ai-generated
-title: Design and implement data pipelines
+title: Design and implement data pipelines with Azure Databricks
 summary: Learn to design and implement robust data pipelines in Azure Databricks using notebooks and Lakeflow Declarative Pipelines, covering orchestration, error handling, and task logic.
 abstract: |
   By the end of this module, you'll be able to:

learn-pr/wwl-databricks/implement-lakeflow-jobs/index.yml

@@ -1,15 +1,15 @@
 ### YamlMime:Module
 uid: learn.wwl.implement-lakeflow-jobs
 metadata:
-  title: Implement Lakeflow Jobs
+  title: Implement Lakeflow Jobs with Azure Databricks
   description: Learn how to create, configure, schedule, and monitor Lakeflow Jobs in Azure Databricks to automate your data pipelines.
   ms.date: 12/07/2025
   author: weslbo
   ms.author: wedebols
   ms.topic: module
   ms.service: azure-databricks
   ai-usage: ai-generated
-title: Implement Lakeflow Jobs
+title: Implement Lakeflow Jobs with Azure Databricks
 summary: This module guides you through the process of implementing Lakeflow Jobs in Azure Databricks. You will learn how to create jobs, configure triggers and schedules, set up alerts, and manage automatic restarts to ensure reliable data pipeline execution.
 abstract: |
   By the end of this module, you'll be able to:

learn-pr/wwl-databricks/implement-manage-data-quality-constraints-unity-catalog/index.yml

@@ -1,15 +1,15 @@
 ### YamlMime:Module
 uid: learn.wwl.implement-manage-data-quality-constraints-unity-catalog
 metadata:
-  title: Implement and Manage Data Quality Constraints
+  title: Implement and Manage Data Quality Constraints with Azure Databricks
   description: Learn how to implement and manage data quality constraints in Azure Databricks using Unity Catalog, including validation checks, schema enforcement, and pipeline expectations.
   ms.date: 12/07/2025
   author: weslbo
   ms.author: wedebols
   ms.topic: module
   ms.service: azure-databricks
   ai-usage: ai-generated
-title: Implement and manage data quality constraints
+title: Implement and manage data quality constraints with Azure Databricks
 summary: This module explores strategies for maintaining high data quality in Azure Databricks. You will learn how to implement validation checks, enforce schemas, manage schema drift, and use pipeline expectations to ensure data integrity throughout your data pipelines.
 abstract: |
   By the end of this module, you'll be able to:

learn-pr/wwl-databricks/select-and-configure-compute/includes/3-configure-compute-performance.md

@@ -4,7 +4,7 @@ Configuring compute resources involves balancing performance requirements with c
 
 Compute performance depends on three key factors working together. Each factor influences how efficiently your workload runs and how much it costs.
 
-![Diagram showing the relationship between cores, memory and storage.](../media/cores-memory-storage.svg)
+![Diagram showing the relationship between cores, memory and storage.](../media/cores-memory-storage.png)
 
 **Total executor cores** determine the maximum parallelism available for processing data. More cores allow Spark to process more tasks simultaneously. A cluster with 8 workers, each having 4 cores, provides 32 cores total for parallel processing.
 
@@ -64,7 +64,7 @@ Enable **decommissioning** when using spot instances to reduce task failures. Wh
 
 Instance pools maintain a set of idle instances ready for immediate use, reducing cluster startup time from minutes to seconds.
 
-![Diagram explaining Azure Databricks Instance Pools usage.](../media/instance-pool-management.svg)
+![Diagram explaining Azure Databricks Instance Pools usage.](../media/instance-pool-management.png)
 
 Configure the **minimum idle instances** to match your typical concurrent cluster needs. If you regularly run three notebooks simultaneously, maintain at least three idle instances. These instances remain available even when not in use, providing instant cluster startup.
 

learn-pr/wwl-databricks/select-and-configure-compute/includes/4-configure-compute-feature-settings.md

@@ -4,7 +4,7 @@ Compute features determine the functional capabilities available to your workloa
 
 **Photon** is a query execution engine that replaces traditional Spark components with optimized native code. When you enable Photon, your compute resource uses this accelerated engine for SQL queries and DataFrame operations.
 
-![Diagram showing a decision tree when to use Photon acceleration.](../media/spark-photon-decision.svg)
+![Diagram showing a decision tree when to use Photon acceleration.](../media/spark-photon-decision.png)
 
 With Photon enabled, queries that involve **complex transformations** run faster. Operations like **joins**, **aggregations**, and **scans** across large tables benefit most from Photon's optimization. Workloads that frequently access disk, process wide tables, or repeatedly transform data also see significant performance gains. For example, a data analyst running hourly aggregation queries across millions of rows will experience faster query completion times with Photon enabled.
 

learn-pr/wwl-databricks/select-and-configure-compute/includes/5-install-libraries-for-compute.md

@@ -77,4 +77,4 @@ To configure the allowlist, metastore admins use Catalog Explorer, selecting the
 
 Different library installation methods suit different scenarios. The following diagram illustrates a decision flow to help you select the appropriate installation approach:
 
-![Diagram showing the different library installation methods.](../media/library-installation.svg)
+![Diagram showing the different library installation methods.](../media/library-installation.png)

learn-pr/wwl-databricks/select-and-configure-compute/includes/6-configure-compute-access-perms.md

@@ -44,7 +44,7 @@ This **permission scoping** enables secure group collaboration on workloads that
 
 The following diagram illustrates how to set up dedicated group access:
 
-![Diagram illustrating how to set up dedicated group access.](../media/group-workspace-setup.svg)
+![Diagram illustrating how to set up dedicated group access.](../media/group-workspace-setup.png)
 
 To create a compute resource dedicated to a group, you must enable Unity Catalog on your workspace and use Databricks Runtime 15.4 or above. The assigned group needs `CAN MANAGE` permission on a workspace folder where members can store notebooks, MLflow experiments, and other workspace artifacts used on the group cluster.
 

learn-pr/wwl-databricks/select-and-configure-compute/index.yml

@@ -1,15 +1,15 @@
 ### YamlMime:Module
 uid: learn.wwl.select-and-configure-compute
 metadata:
-  title: Select and Configure Compute
+  title: Select and Configure Compute in Azure Databricks
   description: Learn how to select and configure appropriate compute resources in Azure Databricks including serverless, classic compute, SQL warehouses, and job clusters. Master performance tuning, access control, and library management.
   ms.date: 12/07/2025
   author: weslbo
   ms.author: wedebols
   ms.topic: module
   ms.service: azure-databricks
   ai-usage: ai-generated
-title: Select and Configure Compute
+title: Select and Configure Compute in Azure Databricks
 summary: Azure Databricks provides multiple compute options optimized for different workloads. This module explores how to choose the right compute type, configure performance settings, manage access permissions, and install libraries. You'll learn when to use serverless versus classic compute, how to optimize clusters for cost and performance, and best practices for securing compute resources.
 abstract: |
   By the end of this module, you'll be able to: