Merge branch 'databricks-demos:main' into main

Author: mkhelghati-db

.gitignore

@@ -0,0 +1,5 @@
+
+.databricks
+databricks.yml
+.DS_Store
+**/.DS_Store

demo-retail/lakehouse-retail-c360/01-Data-ingestion/01.3-DLT-churn-python.py

@@ -332,7 +332,7 @@ def churn_prediction():
 # MAGIC
 # MAGIC Now that these tables are available in our Lakehouse, let's review how we can share them with the Data Scientists and Data Analysts teams.
 # MAGIC
-# MAGIC Jump to the [Governance with Unity Catalog notebook]($../00-churn-introduction-lakehouse) or [Go back to the introduction]($../00-churn-introduction-lakehouse)
+# MAGIC Jump to the [Governance with Unity Catalog notebook]($../02-Data-governance/02.1-UC-data-governance-security-churn) or [Go back to the introduction]($../00-churn-introduction-lakehouse)
 
 # COMMAND ----------
 

demo-retail/lakehouse-retail-c360/02-Data-governance/02-UC-data-governance-security-churn.sql renamed to demo-retail/lakehouse-retail-c360/02-Data-governance/02.1-UC-data-governance-security-churn.sql

@@ -249,8 +249,8 @@ SELECT * FROM churn_users_protected
 -- COMMAND ----------
 
 -- MAGIC %md
--- MAGIC # Next: Start building analysis with Databricks SQL
+-- MAGIC # Next: Start building semantic layer with Databricks SQL
 -- MAGIC
--- MAGIC Now that these tables are available in our Lakehouse and secured, let's see how our Data Analyst team can start leveraging them to run BI workloads
+-- MAGIC Now that these tables are available in our Lakehouse and secured, let's see how we can define our business semantics using metric views.
 -- MAGIC
--- MAGIC Jump to the [BI / Data warehousing notebook]($../03-BI-data-warehousing/03-BI-Datawarehousing) or [Go back to the introduction]($../00-churn-introduction-lakehouse)
+-- MAGIC Jump to the [UC / Metric views notebook]($02.2-UC-metric-views) or [Go back to the introduction]($../00-churn-introduction-lakehouse)