diff --git a/quickstarts/vantagecloud-lake/create-jwt-token-for-api-calls.md b/quickstarts/vantagecloud-lake/create-jwt-token-for-api-calls.md
deleted file mode 100644
index ff61c49542..0000000000
--- a/quickstarts/vantagecloud-lake/create-jwt-token-for-api-calls.md
+++ /dev/null
@@ -1,88 +0,0 @@
----
-sidebar_position: 7
-author: Adam Tworkiewicz
-email: adam.tworkiewicz@teradata.com
-page_last_update: September 6th, 2024
-description: Create JWT tokens to call Lake APIs
-keywords: [data warehouses, compute storage separation, teradata, vantage, cloud data platform, business intelligence, enterprise analytics, jupyter, teradatasql, ipython-sql, teradatasqlalchemy, vantagecloud, vantagecloud lake, ip address, public internet, lake, rest api]
----
-
-import CreateJWT from './_partials/create-jwt.mdx';
-
-# Create JWT tokens to call VantageCloud Lake APIs
-
-## Overview
-
-VantageCloud Lake exposes a set or REST APIs to manage environments. To call VantageCloud Lake REST APIs such as Query Service you need a valid JWT token. This quickstart demonstrates how to create a JWT token and how to use it to call VantageCloud Lake REST APIs.
-
-The process has the following steps:
-1. Generate Personal Access Token and your private key - they are needed to create JWTs
-2. Create a JWT
-3. Call the API
-
-
-## Generate Personal Access Token (PAT) and private key
-
-:::important
-To get a VantageCloud Lake sign-on link and credentials, submit the [contact form](https://www.teradata.com/about-us/contact) to reach out to Teradata team.
-:::
-
-1. Go to the URL provided by Teradata, for example, mycompany.innovationlabs.teradata.com and sign on as a database user who has Console access rights.
-
- :::note
- Click [here](https://login.customer.teradata.com/ext/pwdreset/Identify?AdapterId=CDSCustomer) to reset the organization admin password.
- :::
-
- 
-
- The signing on takes you to VantageCloud Lake welcome page.
-
- 
-
-
-1. Create a Personal Access Token (PAT)
-
- * In the top-right corner click on the user icon and then click on "Account settings"
- 
- * Click on "Create" and then on "Access token"
- 
- * Select the environment you want to use the PAT for, fill out other fields and click Save
- 
- * Copy the PAT displayed on the page
-
-1. Create a private key
-
- * Go back to the "Account settings" page and click "Create" and then "Key"
- 
- * Click on "Generate key pair", name the key "primary", click on "Generate private key", download the `.pem` file and click on "Save"
- 
-
-1. Retrieve the organization id
- * Open a new tab and go to https://\{MY_COMPANY\}.innovationlabs.teradata.com/api/my-organization. Replace `{MY_COMPANY}` with the right name for your Lake deployment
- * Copy the value of the organization id. It should be similar to this: `fd3463dee-3efe-4120-8153-0bb253547df7`.
-
-
-## Use PAT and private key to generate JWT
-
-:::note
-This section assumes that you have you have the following:
-* Private key saved in file called `primary.pem`
-* You now your lake user name that you have used to create the PAT and the primary key
-* You have the PAT
-* You have your organization id
-:::
-
-The code demonstrates how to generate a JWT and call Query Service with the JWT. Make sure you set the correct path to `primary.pem` file containing the private key and set the correct values for:
-* `sub` - the user name
-* `pat` - PAT
-* `org_id` - organization id
-
-
-
-## Summary
-
-This quickstart describes how to create JWT tokens to call VantageCloud Lake REST APIs.
-
-## Further reading
-
-* [Teradata VantageCloud Lake documentation](https://docs.teradata.com/r/Teradata-VantageCloud-Lake/Getting-Started-First-Sign-On-by-Organization-Admin)
\ No newline at end of file
diff --git a/quickstarts/vantagecloud-lake/vantagecloud-lake-demo-jupyter-azure.md b/quickstarts/vantagecloud-lake/vantagecloud-lake-demo-jupyter-azure.md
deleted file mode 100644
index 23aad66b41..0000000000
--- a/quickstarts/vantagecloud-lake/vantagecloud-lake-demo-jupyter-azure.md
+++ /dev/null
@@ -1,165 +0,0 @@
----
-sidebar_position: 6
-author: Daniel Herrera
-email: daniel.herrera2@teradata.com
-page_last_update: January 26th, 2024
-description: Run Teradata Jupyter Notebook Demos for VantageCloud Lake in Azure
-keywords: [data warehouses, compute storage separation, teradata, vantage, cloud data platform, business intelligence, enterprise analytics, jupyter, teradatasql, ipython-sql, cloud computing, machine learning, sagemaker, vantagecloud, vantagecloud lake, lake]
----
-
-import VantageCloudLakeRequest from './_partials/vantagecloud-lake-request.md';
-
-# Run Teradata Jupyter Notebook Demos for VantageCloud Lake in Microsoft Azure
-
-
-## Overview
-This quickstart details the process for running the [Teradata Jupyter Notebook Demos for VantageCloud Lake](https://github.com/Teradata/lake-demos), on Microsoft Azure.
-
-## Prerequisites
-* Access to a Microsoft Azure account
-* Access to a VantageCloud Lake environment
-
-
-## Microsoft Azure setup
-In this section we will cover in detail each of the steps below:
-
-* Create a Microsoft Azure Web App based on Teradata Jupyter Lab extensions Docker image
-* Configure Jupyter Lab extensions Azure Web App
-* Load Vantagecloud Lake demos to Jupyter Lab extensions Azure Web App
-* Find the IP of the Jupyter Lab extensions Azure Web App
-
-### Create a Microsoft Azure Web App based on Teradata Jupyter Lab extensions Docker image
-* Login to Microsoft Azure and click on "APP Services"
- 
-
-* In "App Services" click Web App
- 
-
-* On the "Basics" tab:
- * Select the appropriate resource group from the dropdown, or create a new one
- * Enter a name for your web app.
- * Select "Docker Container" in the "Publish" radio button options
- * Select "Linux" as the operating system
- * Select the appropriate region from the dropdown
- * Select the appropriate App Service plan. If you don't have one, a new one will be created with default configurations
-
- :::tip
- For purposes of the VantageCloud Lake demo redundancy is not needed
- :::
-
- * After completing this tab, click the "Docker" tab to continue
- 
-
-* On the "Docker" tab:
- * Select "Single Container" from the dropdown
- * In the "Image Source" dropdown select "Docker Hub"
- * In the "Access Type" dropdown select "Public"
- * In "Image and tag" type `teradata/jupyterlab-extensions:latest`
- :::tip
- A startup command is not needed for this App Service
- :::
- * Select the "Review + Create" tab to continue
- 
- * In the "Review + Create" tab click the "Create" button
- 
- * When the deployment is complete click the "Go to Resource" button
- 
-
-### Configure Jupyter Lab extensions Azure Web App
-* Select Configuration on the right panel
- 
-
-* Add the following Application Settings
- | **Application Setting** | **Value** |
- |--------------|-----------|
- | **"accept_license"** | Y |
- | **"WEBSITES_PORT"** | 8888 |
- | **"JUPYTER_TOKEN"** | Define the Jupyter Lab access token that you would like to use. |
-
- :::info
- If you don't include the "JUPYTER_TOKEN" configuration, the container will generate a new token and log it to the console. You will need to retrieve it from the application logs. If you include the "JUPYTER_TOKEN" configuration key but leave the value blank, the system will set the token as an empty string, resulting in an unprotected Jupyter Lab environment without any token security.
- :::
-
-* Click on save, your app will be restarted
- 
-
-* Return to the Overview tab on the right panel
-
-### Load Vantagecloud Lake demos to Jupyter Lab extensions Azure Web App
-* Click on Default domain
- 
-
-* On the Jupyter Lab start dialogue enter the defined Jupyter token and click Log in
- 
-
-* On the Jupyter Lab console click on the git icon
- 
-
-* Copy the following URI in the corresponding field
- * `https://github.com/Teradata/lake-demos.git`
-* Click Clone
- 
-
-* On the Jupyter Lab console click in the `lake-demos` folder
- 
-
- 
-
-### Find the IP of the Jupyter Lab extensions Azure Web App
-* In JupyterLab open a notebook with Teradata Python kernel and run the following command to find your notebook instance's IP address.
- ``` python , id="lakedemos_azure_first_config", role="emits-gtm-events, content-editable"
- import requests
- def get_public_ip():
- try:
- response = requests.get('https://api.ipify.org')
- return response.text
- except requests.RequestException as e:
- return "Error: " + str(e)
- my_public_ip = get_public_ip()
- print("My Public IP is:", my_public_ip)
- ```
-
-* The next step is whitelist this IP in your VantageCloud Lake environment to allow the connection
-* This is for purposes of this guide and the notebook demos. For production environments, a more robust networking setting might be needed
-* Azure App Service offers, as well, a list of all possible IP addresses that the service might expose. This is under the overview tab
- 
-
-## VantageCloud Lake Configuration
-* In the VantageCloud Lake environment, under settings, add the IP of your notebook instance
- :::tip
- A lake environment supports multiple address whitelisting
- :::
-
- 
-
-## Jupyter Notebook Demos for VantageCloud Lake
-
-### Configurations
-* [vars.json](https://github.com/Teradata/lake-demos/blob/main/vars.json) should be edited to match the configuration of your VantageCloud Lake environment
- 
-
-* Especifically the following values should be added
- | **Variable** | **Value** |
- |--------------|-----------|
- | **"host"** | Public IP value from your VantageCloud Lake environment |
- | **"UES_URI"** | Open Analytics from your VantageCloud Lake environment |
- | **"dbc"** | The master password of your VantageCloud Lake environment. |
-
-* You'll see that in the sample vars.json, the passwords of all users are defaulted to "password", this is just for illustration purposes, you should change all of these password fields to strong passwords, secure them as necessary, and follow other password management best practices.
- :::info
- Remember to change all passwords in the vars.json file.
- :::
-
-## Run demos
-Open and execute all the cells in *0_Demo_Environment_Setup.ipynb* to setup your environment. Followed by *1_Demo_Setup_Base_Data.ipynb* to load the base data required for demo.
-
-To learn more about the demo notebooks, go to [Teradata Lake demos](https://github.com/Teradata/lake-demos) page on GitHub.
-
-## Summary
-
-In this quick start we learned how to run Jupyter notebook demos for VantageCloud Lake in Microsoft Azure.
-
-## Further reading
-
-* [Teradata VantageCloud Lake documentation](https://docs.teradata.com/r/Teradata-VantageCloud-Lake/Getting-Started-First-Sign-On-by-Organization-Admin)
-* [Use Vantage from a Jupyter notebook](../analyze-data/jupyter.md)
\ No newline at end of file
diff --git a/quickstarts/vantagecloud-lake/vantagecloud-lake-demo-jupyter-sagemaker.md b/quickstarts/vantagecloud-lake/vantagecloud-lake-demo-jupyter-sagemaker.md
deleted file mode 100644
index 20826d650e..0000000000
--- a/quickstarts/vantagecloud-lake/vantagecloud-lake-demo-jupyter-sagemaker.md
+++ /dev/null
@@ -1,198 +0,0 @@
----
-sidebar_position: 4
-author: Daniel Herrera
-email: daniel.herrera2@teradata.com
-page_last_update: January 16th, 2024
-description: Run Teradata Jupyter Notebook Demos for VantageCloud Lake in SageMaker
-keywords: [data warehouses, compute storage separation, teradata, vantage, cloud data platform, business intelligence, enterprise analytics, jupyter, teradatasql, ipython-sql, cloud computing, machine learning, sagemaker, vantagecloud, vantagecloud lake, lake]
----
-
-# Run Teradata Jupyter Notebook Demos for VantageCloud Lake in Amazon SageMaker
-
-## Overview
-This quickstart details the process for running the [Teradata Jupyter Notebook Demos for VantageCloud Lake](https://github.com/Teradata/lake-demos), on Amazon SageMaker, the AI/ML platform from AWS.
-
-## Prerequisites
-* Teradata modules for Jupyter (download [here](https://downloads.teradata.com/download/tools/vantage-modules-for-jupyter), registration required)
-* AWS account with access to S3 and SageMaker
-* [Access to a VantageCloud Lake environment](./getting-started-with-vantagecloud-lake.md)
-
-## AWS environment set-up
-In this section we will cover in detail each of the steps below:
-
-* Upload the Teradata modules for Jupyter to a S3 bucket
-* Create an IAM role for your Jupyter notebook instance
-* Create a lifecycle configuration for your Jupyter notebook instance
-* Create Jupyter notebook instance
-* Find the IP CIDR of your Jupyter notebook instance
-
-### Upload the Teradata modules for Jupyter to an S3 bucket
-* On AWS S3 create a bucket and keep note of the assigned name
-* Default options are appropiate for this bucket
-* In the created bucket upload the Teradata modules for Jupyter
- 
-
-### Create an IAM role for your Jupyter Notebooks instance
-* On SageMaker navigate to the role manager
- 
-* Create a new role (if not already defined)
-* For purposes of this guide the role created is assigned the data scientist persona
- 
-* On the settings, it is appropiate to keep the defaults
-* In the corresponding screen define the bucket where you uploaded the Teradata Jupyter modules
- 
-* In the next configuration we add the corresponding policies for access to the S3 bucket
- 
-
-### Create lifecycle configuration for your Jupyter Notebooks instance
-* On SageMaker navigate to lifecycle configurations and click on create
- 
-* Define a lifecycle configuration with the following scripts
- * When working from a Windows environment, we recommend copying the scripts into the lifecycle configuration editor line by line. Press 'Enter' after each line directly in the editor to avoid copying issues. This approach helps prevent carriage return errors that can occur due to encoding differences between Windows and Linux. Such errors often manifest as "/bin/bash^M: bad interpreter" and can disrupt script execution.
- 
-
- * On create script:
-
- ``` bash , id="sagemaker_first_config", role="content-editable emits-gtm-events"
- #!/bin/bash
-
- set -e
-
- # This script installs a custom, persistent installation of conda on the Notebook Instance's EBS volume, and ensures
- # that these custom environments are available as kernels in Jupyter.
-
-
- sudo -u ec2-user -i <<'EOF'
- unset SUDO_UID
- # Install a separate conda installation via Miniconda
- WORKING_DIR=/home/ec2-user/SageMaker/custom-miniconda
- mkdir -p "$WORKING_DIR"
- wget https://repo.anaconda.com/miniconda/Miniconda3-4.6.14-Linux-x86_64.sh -O "$WORKING_DIR/miniconda.sh"
- bash "$WORKING_DIR/miniconda.sh" -b -u -p "$WORKING_DIR/miniconda"
- rm -rf "$WORKING_DIR/miniconda.sh"
- # Create a custom conda environment
- source "$WORKING_DIR/miniconda/bin/activate"
- KERNEL_NAME="teradatasql"
-
- PYTHON="3.8"
- conda create --yes --name "$KERNEL_NAME" python="$PYTHON"
- conda activate "$KERNEL_NAME"
- pip install --quiet ipykernel
-
- EOF
- ```
-
- * On start script (In this script substitute name of your bucket and confirm version of Jupyter modules)
-
- ``` bash , role="content-editable emits-gtm-events"
- #!/bin/bash
-
- set -e
-
- # This script installs Teradata Jupyter kernel and extensions.
-
-
- sudo -u ec2-user -i <<'EOF'
- unset SUDO_UID
-
- WORKING_DIR=/home/ec2-user/SageMaker/custom-miniconda
-
- source "$WORKING_DIR/miniconda/bin/activate" teradatasql
-
- # Install teradatasql, teradataml, and pandas in the teradatasql environment
- pip install teradataml
- pip install pandas
-
- # fetch Teradata Jupyter extensions package from S3 and unzip it
- mkdir -p "$WORKING_DIR/teradata"
- aws s3 cp s3://resources-jp-extensions/teradatasqllinux_3.4.1-d05242023.zip "$WORKING_DIR/teradata"
- cd "$WORKING_DIR/teradata"
- unzip -o teradatasqllinux_3.4.1-d05242023
- cp teradatakernel /home/ec2-user/anaconda3/condabin
- jupyter kernelspec install --user ./teradatasql
- source /home/ec2-user/anaconda3/bin/activate JupyterSystemEnv
-
- # Install other Teradata-related packages
- pip install teradata_connection_manager_prebuilt-3.4.1.tar.gz
- pip install teradata_database_explorer_prebuilt-3.4.1.tar.gz
- pip install teradata_preferences_prebuilt-3.4.1.tar.gz
- pip install teradata_resultset_renderer_prebuilt-3.4.1.tar.gz
- pip install teradata_sqlhighlighter_prebuilt-3.4.1.tar.gz
-
- conda deactivate
- EOF
- ```
-
-### Create Jupyter Notebooks instance
-* On SageMaker navigate Notebooks, Notebook instances, create notebook instance
-* Choose a name for your notebook instance, define size (for demos the smaller available instance is enough)
-* Click in additional configurations and assign the recently created lifecycle configuration
- 
-* Click in additional configurations and assign the recently created lifecycle configuration
-* Assign the recently created IAM role to the notebook instance
- 
-
-* Paste the following link https://github.com/Teradata/lake-demos as the default github repository for the notebook instance
- 
-
-## Find the IP CIDR of your Jupyter Notebooks instance
-* Once the instance is running click on open JupyterLab
- 
-
- 
-
-* On JupyterLab open a notebook with Teradata Python kernel and run the following command for finding your notebook instance IP address.
-** We will whitelist this IP in your VantageCloud Lake environment in order to allow the connection.
-** This is for purposes of this guide and the notebooks demos. For production environments, a configuration of VPCs, Subnets and Security Groups might need to be configured and whitelisted.
-
-``` python , role="content-editable"
-import requests
-def get_public_ip():
- try:
- response # requests.get('https://api.ipify.org')
- return response.text
- except requests.RequestException as e:
- return "Error: " + str(e)
-my_public_ip = get_public_ip()
-print("My Public IP is:", my_public_ip)
-```
-
-## VantageCloud Lake Configuration
-* In the VantageCloud Lake environment, under settings, add the IP of your notebook instance
- 
-
-## Jupyter Notebook Demos for VantageCloud Lake
-
-### Configurations
-* The file [vars.json file](https://github.com/Teradata/lake-demos/blob/main/vars.json) should be edited to match the configuration of your VantageCloud Lake environment
- 
-
-* Especifically the following values should be added
-
-
- | **Variable** | **Value**|
- |--------------|----------|
- |**"host"**|Public IP value from your VantageCloud Lake environment|
- |**"UES_URI"** |Open Analytics from your VantageCloud Lake environment|
- |**"dbc"**|The master password of your VantageCloud Lake environment|
-
-
- :::info
- Remember to change all passwords in the vars.json file.
- :::
-
-* You'll see that in the sample vars.json, the passwords of all users are defaulted to "password", this is just for illustration purposes, you should change all of these password fields to strong passwords, secure them as necessary, and follow other password management best practices.
-
-## Run demos
-Open and execute all the cells in **0_Demo_Environment_Setup.ipynb** to setup your environment. Followed by **1_Demo_Setup_Base_Data.ipynb** to load the base data required for demo.
-
-To learn more about the demo notebooks, go to [Teradata Lake demos](https://github.com/Teradata/lake-demos) page on GitHub.
-
-## Summary
-
-In this quick start we learned how to run Jupyter notebook demos for VantageCloud Lake in Amazon SageMaker.
-
-## Further reading
-
-* [Teradata VantageCloud Lake documentation](https://docs.teradata.com/r/Teradata-VantageCloud-Lake/Getting-Started-First-Sign-On-by-Organization-Admin)
-* [Use Vantage from a Jupyter notebook](../analyze-data/jupyter.md)
\ No newline at end of file