diff --git a/.github/MLOps_Engineer/Utilities/Bash/utilsSetEnvVariables.sh b/.github/MLOps_Engineer/Utilities/Bash/utilsSetEnvVariables.sh
index 78b9d9a3..2455334f 100644
--- a/.github/MLOps_Engineer/Utilities/Bash/utilsSetEnvVariables.sh
+++ b/.github/MLOps_Engineer/Utilities/Bash/utilsSetEnvVariables.sh
@@ -16,6 +16,11 @@ DATABRICKS_INSTANCE="$(az databricks workspace list -g $RESOURCE_GROUP_NAME --qu
 WORKSPACE_ID=$(az databricks workspace list -g $RESOURCE_GROUP_NAME --query "[].id" -o tsv)
 AZ_KEYVAULT_NAME=$(az keyvault list -g $RESOURCE_GROUP_NAME --query "[].name" -o tsv)
 SUBSCRIPTION_ID=$( az account show --query id -o tsv )
+echo $SUBSCRIPTION_ID
+echo $DATABRICKS_ORDGID
+echo $WORKSPACE_ID
+echo $AZ_KEYVAULT_NAME
+echo $SUBSCRIPTION_ID
 #DATABRICKS_TOKEN=$(az keyvault secret show --name "dbkstoken" --vault-name $AZ_KEYVAULT_NAME --query "value" -o tsv)
 
 
diff --git a/README.md b/README.md
index 0bb82165..cbfb815f 100644
--- a/README.md
+++ b/README.md
@@ -152,6 +152,22 @@ https://learn.microsoft.com/en-us/azure/databricks/machine-learning/mlops/deploy
 
 ---
 ---
+## Feature Store Integration 
+
+In an organization, thousands of features are buried in different scripts and in different formats; they are not captured, organized, or preserved, and thus cannot be reused and leveraged by teams other than those who generated them.
+
+Because feature engineering is so important for machine learning models and features cannot be shared, data scientists must duplicate their feature engineering efforts across teams.
+
+To solve those problems, a concept called feature store was developed, so that:
+
+- Features are centralized in an organization and can be reused
+- Features can be served in real-time with low latency
+
+![image](https://user-images.githubusercontent.com/108273509/216114586-0c4dea68-a98c-4cf6-938a-ceecf11b12a8.png)
+
+---
+---
+
 
 ## Create Repository
 <details open>