diff --git a/quickstarts/analyze-data/integrate-teradata-jupyter-extensions-with-google-vertex-ai.md b/quickstarts/analyze-data/integrate-teradata-jupyter-extensions-with-google-vertex-ai.md
deleted file mode 100644
index b5d20317c3c..00000000000
--- a/quickstarts/analyze-data/integrate-teradata-jupyter-extensions-with-google-vertex-ai.md
+++ /dev/null
@@ -1,178 +0,0 @@
----
-sidebar_position: 11
-author: Hailing Jiang
-email: Hailing.Jiang@teradata.com
-page_last_update: June 28th, 2022
-description: Integrate Teradata Jupyter extensions with Google Vertex AI
-keywords: [data warehouses, compute storage separation, teradata, vantage, cloud data platform, business intelligence, enterprise analytics, jupyter, teradatasql, ipython-sql, teradatasqlalchemy]
----
-import TrialDocsNote from '../_partials/teradata_trial.mdx'
-import JupyterTrialsNote from '../_partials/jupyter_notebook_trials_note.mdx';
-
-# Integrate Teradata Jupyter extensions with Google Vertex AI
-
-
-
-
-## Overview
-Teradata Jupyter extensions provide Teradata SQL kernel and several UI extensions to allow users to easily access and navigate Teradata database from Jupyter envioronment. Google Vertex AI is Google Cloud's new unified ML platform. Vertex AI Workbench provides a Jupyter-base development environment for the entire data science workflow. This article describes how to integate our Jupyter extensions with Vertex AI Workbench so that Vertex AI users can take advantage of our Teradata extensions in their ML pipeline.
-
-Vertex AI workbench supports two types of notebooks: managed notebooks and user-managed notebooks. Here we will focus on user-managed notebooks. We will show two ways to integrate our Jupyter extensions with user-managed notebooks: use startup script to install our kernel and extensions or use custom container.
-
-## Prerequisites
-
-* Access to a Teradata Vantage instance
-
-* Google Cloud account with Vertex AI enabled
-* Google cloud storage to store startup scripts and Teradata Jupyter extension package
-
-## Integration
-
-There are two ways to run Teradata Jupyter Extensions in Vertex AI:
-
-1. [Use startup script](#use-startup-script)
-2. [Use custom container](#use-custom-container)
-
-These two integration methods are described below.
-
-### Use startup script
-
-When we create a new notebook instance, we can specify a startup script. This script runs only once after the instance is created. Here are the steps:
-
-1. Download Teradata Jupyter extensions package
-
-Go to [Vantage Modules for Jupyter](https://downloads.teradata.com/download/tools/vantage-modules-for-jupyter) page to download the Teradata Jupyter extensions package bundle Linux version.
-
-2. Upload the package to a Google Cloud storage bucket
-
-3. Write a startup script and upload it to cloud storage bucket
-
-Below is a sample script. It fetches Teradata Jupyter extension package from cloud storage bucket and installs Teradata SQL kernel and extensions.
-
-``` bash , role="content-editable"
-#! /bin/bash
-
-cd /home/jupyter
-mkdir teradata
-cd teradata
-gsutil cp gs://teradata-jupyter/* .
-unzip teradatasql*.zip
-
-# Install Teradata kernel
-cp teradatakernel /usr/local/bin
-
-jupyter kernelspec install ./teradatasql --prefix=/opt/conda
-
-# Install Teradata extensions
-pip install --find-links . teradata_preferences_prebuilt
-pip install --find-links . teradata_connection_manager_prebuilt
-pip install --find-links . teradata_sqlhighlighter_prebuilt
-pip install --find-links . teradata_resultset_renderer_prebuilt
-pip install --find-links . teradata_database_explorer_prebuilt
-
-# PIP install the Teradata Python library
-pip install teradataml
-
-# Install Teradata R library (optional, uncomment this line only if you use an environment that supports R)
-#Rscript -e "install.packages('tdplyr',repos=c('https://r-repo.teradata.com','https://cloud.r-project.org'))"
-```
-
-
-4. Create a new notebook and add the startup script from cloud storage bucket
-
-
-5. It may take a few minutes for the notebook creation process to complete. When it is done, click on `Open notebook`.
-
-
-### Use custom container
-
-Another option is to provide a custom container when creating a notebook.
-
-1. Download Teradata Jupyter extensions package
-
-Go to [Vantage Modules for Jupyter](https://downloads.teradata.com/download/tools/vantage-modules-for-jupyter) page to download the Teradata Jupyter extensions package bundle Linux version.
-
-2. Copy this package to your work directory and unzip it
-
-3. Build custom Docker image
-
-The custom container must expose a service on port 8080. It is recommended to create a container derived from a Google Deep Learning Containers image, because those images are already configured to be compatible with user-managed notebooks.
-
-Below is a sample Dockerfile you can use to build a Docker image with Teradata SQL kernel and extensions installed:
-
-``` bash
-# Use one of the deep learning images as base image
-# if you need both Python and R, use one of the R images
-FROM gcr.io/deeplearning-platform-release/r-cpu:latest
-
-USER root
-
-##############################################################
-# Install kernel and copy supporting files
-##############################################################
-
-# Copy the kernel
-COPY ./teradatakernel /usr/local/bin
-
-RUN chmod 755 /usr/local/bin/teradatakernel
-
-# Copy directory with kernel.json file into image
-COPY ./teradatasql teradatasql/
-
-# Copy notebooks and licenses
-COPY ./notebooks/ /home/jupyter
-COPY ./license.txt /home/jupyter
-COPY ./ThirdPartyLicenses/ /home/jupyter
-
-# Install the kernel file to /opt/conda jupyter lab instance
-RUN jupyter kernelspec install ./teradatasql --prefix=/opt/conda
-
-##############################################################
-# Install Teradata extensions
-##############################################################
-
-RUN pip install --find-links . teradata_preferences_prebuilt && \
- pip install --find-links . teradata_connection_manager_prebuilt && \
- pip install --find-links . teradata_sqlhighlighter_prebuilt && \
- pip install --find-links . teradata_resultset_renderer_prebuilt && \
- pip install --find-links . teradata_database_explorer_prebuilt
-
-# Give back ownership of /opt/conda to jovyan
-RUN chown -R jupyter:users /opt/conda
-
-# PIP install the Teradata Python libraries
-RUN pip install teradataml
-
-# Install Teradata R library (optional, include it only if you use a base image that supports R)
-RUN Rscript -e "install.packages('tdplyr',repos=c('https://r-repo.teradata.com','https://cloud.r-project.org'))"
-```
-
-4. In your work directory (where you unzipped Teradata Jupyter extensions package), run `docker build` to build the image:
-
-``` bash , id="jupyterlab_ext_on_vertex_first_run", role="content-editable emits-gtm-events
-docker build -f Dockerfile imagename:imagetag .
-```
-
-5. Push the docker image to Google container registry or artifact registry
-
-Please refer to the following documentations to push docker image to registry:
-
-* [Container Registry: Pushing and pulling images](https://cloud.google.com/container-registry/docs/pushing-and-pulling)
-* [Artifact Registry: Pushing and pulling images](https://cloud.google.com/artifact-registry/docs/docker/pushing-and-pulling?hl=en)
-
-6. Create a new notebook
-
-In `Environment` section, set `custom container` field to the location of your newly created custom container:
-
-
-## Further reading
-* [Teradata Jupyter Extensions Website](https://teradata.github.io/jupyterextensions)
-* [Teradata Vantage™ Modules for Jupyter Installation Guide](https://docs.teradata.com/r/KQLs1kPXZ02rGWaS9Ktoww/root)
-* [Teradata® Package for Python User Guide](https://docs.teradata.com/r/1YKutX2ODdO9ppo_fnguTA/root)
-* [Vertex AI documentation: Create a custom container image for training](https://cloud.google.com/vertex-ai/docs/training/create-custom-container)
-* [Vertex AI documentation: Create a user-managed notebooks instance by using a custom container](https://cloud.google.com/vertex-ai/docs/workbench/user-managed/custom-container)
-* [Vertex AI documentation: Create a user-managed notebooks instance](https://cloud.google.com/vertex-ai/docs/workbench/user-managed/create-new)
-
-import CommunityLinkPartial from '../_partials/community_link.mdx';
-
-
diff --git a/quickstarts/analyze-data/integrate-teradata-jupyter-extensions-with-sagemaker.md b/quickstarts/analyze-data/integrate-teradata-jupyter-extensions-with-sagemaker.md
index 40407446bbf..673f1a6ffa1 100644
--- a/quickstarts/analyze-data/integrate-teradata-jupyter-extensions-with-sagemaker.md
+++ b/quickstarts/analyze-data/integrate-teradata-jupyter-extensions-with-sagemaker.md
@@ -1,57 +1,73 @@
---
sidebar_position: 11
-author: Hailing Jiang
-email: Hailing.Jiang@teradata.com
-page_last_update: September 27th, 2022
+author: Hailing Jiang, Janeth Graziani
+email: Hailing.Jiang@teradata.com, developer.relations@teradata.com
+page_last_update: July 9, 2026
description: Integrate Teradata Jupyter extensions with SageMaker notebook instance
-keywords: [data warehouses, compute storage separation, teradata, vantage, cloud data platform, business intelligence, enterprise analytics, jupyter, teradatasql, ipython-sql, teradatasqlalchemy]
+keywords: [data warehouses, compute storage separation, teradata, cloud data platform, business intelligence, enterprise analytics, jupyter, teradatasql, ipython-sql, teradatasqlalchemy]
---
import TrialDocsNote from '../_partials/teradata_trial.mdx'
import JupyterTrialsNote from '../_partials/jupyter_notebook_trials_note.mdx';
-# Integrate Teradata Jupyter extensions with SageMaker notebook instance
+# Integrate Teradata Jupyter extensions with SageMaker AI notebook instance
### Overview
-Teradata Jupyter extensions provide Teradata SQL kernel and several UI extensions to allow users to easily asccess and navigate Teradata database from Jupyter envioronment. This article describes how to integate our Jupyter extensions with SageMaker notebook instance.
+Teradata Jupyter extensions provide Teradata SQL kernel and several UI extensions to allow users to easily access and navigate Teradata database from Jupyter environment. This article describes how to integrate our Jupyter extensions with SageMaker AI notebook instance.
### Prerequisites
-* Access to a Teradata Vantage instance
+* Access to a Teradata instance
-* AWS account
-* AWS S3 bucket to store lifecycle configuration scripts and Teradata Jupyter extension package
+* AWS account with permissions to create SageMaker notebook instances and IAM roles
+* AWS S3 bucket to store the Teradata Jupyter extension package
### Integration
-SageMaker supports customization of notebook instances using lifecycle configuration scripts. Below we will demo how to use lifecycle configuration scripts to install our Jupyter kernel and extensions in a notebook instance.
+SageMaker AI supports customization of notebook instances using lifecycle configuration scripts. Below we will demo how to use lifecycle configuration scripts to install our Jupyter kernel and extensions in a notebook instance.
### Steps to integrate with notebook instance
1. Download Teradata Jupyter extensions package
-Download Linux version from https://downloads.teradata.com/download/tools/vantage-modules-for-jupyter and upload it to an S3 bucket. This zipped package contains Teradata Jupyter kernel and extensions. Each extension has 2 files, the one with "_prebuilt" in the name is prebuilt extension which can be installed using PIP, the other one is source extension that needs to be installed using "jupyter labextension". It is recommended to use prebuilt extensions.
+ Download the Linux version from https://downloads.teradata.com/download/tools/vantage-modules-for-jupyter and upload the zip file to your S3 bucket. The package contains the Teradata Jupyter kernel and extensions as `.whl` files.
-2. Create a lifecycle configuration for notebook instance
+
+
+2. **Grant SageMaker access to your S3 bucket**
+
+ The SageMaker execution role needs permission to read from your S3 bucket. In the AWS IAM console, find the execution role associated with your notebook instance (typically named `AmazonSageMaker-ExecutionRole-...`) and attach the `AmazonS3FullAccess` policy.
+
+
+
+
+
+
+3. Create a lifecycle configuration for notebook instance
-Here are sample scripts that fetches the Teradata package from S3 bucket and installs Jupyter kernel and extensions. Note that on-create.sh creates a custom conda env that persists on notebook instance's EBS volume so that the installation will not get lost after notebook restarts. on-start.sh installs Teradata kernel and extensions to the custom conda env.
+
+In the SageMaker console, navigate to **Lifecycle configurations** and create a new configuration. You will see two script tabs: **Create notebook** and **Start notebook**. Paste the scripts below into the corresponding tabs.
+
+
+
+`on-create.sh` runs once when the notebook instance is first created. It installs a persistent conda environment on the EBS volume so the installation survives notebook restarts.
+
on-create.sh
``` bash , role="content-editable
#!/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
@@ -63,78 +79,83 @@ 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 "$KERNEL_NAME"
pip install --quiet ipykernel
-
EOF
```
+`on-start.sh` runs each time the notebook instance starts. It fetches the Teradata package from S3 and installs the Jupyter kernel and extensions. Replace `` with your bucket name and update the zip filename and `.whl` version numbers to match the version you downloaded.
+
on-start.sh
``` bash , role="content-editable"
#!/bin/bash
-
+
set -e
-
+
# This script installs Teradata Jupyter kernel and extensions.
-
-
+
sudo -u ec2-user -i <<'EOF'
unset SUDO_UID
-
+
WORKING_DIR=/home/ec2-user/SageMaker/custom-miniconda
-
+
source "$WORKING_DIR/miniconda/bin/activate" teradatasql
-
+
# fetch Teradata Jupyter extensions package from S3 and unzip it
mkdir -p "$WORKING_DIR/teradata"
-aws s3 cp s3://sagemaker-teradata-bucket/teradatasqllinux_3.3.0-ec06172022.zip "$WORKING_DIR/teradata"
+aws s3 cp s3:///teradatasqllinux_4.0.3-d03302026.zip "$WORKING_DIR/teradata"
cd "$WORKING_DIR/teradata"
-
-unzip -o teradatasqllinux_3.3.0-ec06172022.zip
-
+unzip -o teradatasqllinux_4.0.3-d03302026.zip
+
# install Teradata kernel
cp teradatakernel /home/ec2-user/anaconda3/condabin
+source /home/ec2-user/anaconda3/bin/activate JupyterSystemEnv
jupyter kernelspec install --user ./teradatasql
-
+
# install Teradata Jupyter extensions
-source /home/ec2-user/anaconda3/bin/activate JupyterSystemEnv
-
-pip install teradata_connection_manager_prebuilt-3.3.0.tar.gz
-pip install teradata_database_explorer_prebuilt-3.3.0.tar.gz
-pip install teradata_preferences_prebuilt-3.3.0.tar.gz
-pip install teradata_resultset_renderer_prebuilt-3.3.0.tar.gz
-pip install teradata_sqlhighlighter_prebuilt-3.3.0.tar.gz
-
+pip install teradata_connection_manager-4.0.3-py3-none-any.whl
+pip install teradata_database_explorer-4.0.3-py3-none-any.whl
+pip install teradata_preferences-4.0.3-py3-none-any.whl
+pip install teradata_resultset_renderer-4.0.3-py3-none-any.whl
+pip install teradata_sqlhighlighter-4.0.3-py3-none-any.whl
+
conda deactivate
EOF
```
-3. Create a notebook instance. Please select 'Amazon Linux 2, Jupyter Lab3' for Platform identifier and select the lifecycle configuration created in step 2 for Lifecycle configuration.
- 
+4. **Create a notebook instance**
-You might also need to add vpc, subnet and security group in 'Network' section to gain access to Teradata databases.
+ In the SageMaker console, create a new notebook instance. Select `Amazon Linux 2, Jupyter Lab 4` for the Platform identifier and select the lifecycle configuration created in step 3.
-4. Wait until notebook instance Status turns 'InService', click 'Open JupyterLab' to open the notebook.
- 
+ Under **Permissions and encryption**, select the IAM execution role that has S3 access.
+ Under **Network**, select your VPC, subnet, and the default security group. Enable **Direct internet access** so the lifecycle scripts can reach S3 and install packages.
- Access the demo notebooks to get usage tips
+ 
+
+ 
+
+
+5. **Open the notebook**
+
+ Wait until the notebook instance status turns **InService**, then click **Open JupyterLab**.
+
+ 
+
+ Access the demo notebooks to get started. Navigate to `custom-miniconda/teradata/notebooks/sql/GettingStarted.ipynb`
+
### Further reading
* [Teradata Jupyter Extensions Website](https://teradata.github.io/jupyterextensions)
-* [Teradata Vantage™ Modules for Jupyter Installation Guide](https://docs.teradata.com/r/KQLs1kPXZ02rGWaS9Ktoww/root)
+* [Teradata Modules for Jupyter Installation Guide](https://docs.teradata.com/r/KQLs1kPXZ02rGWaS9Ktoww/root)
* [Teradata® Package for Python User Guide](https://docs.teradata.com/r/1YKutX2ODdO9ppo_fnguTA/root)
* [Customize a Notebook Instance Using a Lifecycle Configuration Script](https://docs.aws.amazon.com/sagemaker/latest/dg/notebook-lifecycle-config.html)
-* [amazon sagemaker notebook instance lifecycle config samples](https://github.com/aws-samples/amazon-sagemaker-notebook-instance-lifecycle-config-samples/blob/master/scripts/persistent-conda-ebs/on-create.sh)
-
-import CommunityLinkPartial from '../_partials/community_link.mdx';
-
-
+* [amazon ai notebook instance lifecycle config samples](https://github.com/aws-samples/amazon-sagemaker-notebook-instance-lifecycle-config-samples/blob/master/scripts/persistent-conda-ebs/on-create.sh)
\ No newline at end of file
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In this quick start, we learned how to ingest and catalog data from Teradata to Amazon S3 with AWS Glue Scripts.
-### Further reading
-* [Integrate Teradata with Google Cloud Data Catalog](./integrate-teradata-vantage-with-google-cloud-data-catalog.md)
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----
-sidebar_position: 17
-author: Wenjie Tehan
-email: wenjie.tehan@teradata.com
-page_last_update: February 14th, 2022
-description: Integrate Teradata Vantage with Google Cloud Data Catalog.
-keywords: [data warehouses, compute storage separation, teradata, vantage, cloud data platform, object storage, business intelligence, enterprise analytics, google cloud, google cloud data catalog.]
----
-
-# Integrate Teradata Vantage with Google Cloud Data Catalog
-
-### Overview
-
-This article describes the process to connect Teradata Vantage with Google Cloud Data Catalog using the [Data Catalog Teradata Connector on GitHub](https://github.com/GoogleCloudPlatform/datacatalog-connectors-rdbms/tree/master/google-datacatalog-teradata-connector), and then explore the metadata of the Vantage tables via Data Catalog.
-
-
-
-* Scrape: Connect to Teradata Vantage and retrieve all the available metadata
-* Prepare: Transform metadata in Data Catalog entities and create Tags
-* Ingest: Send the Data Catalog entities to the Google Cloud project
-
-### About Google Cloud Data Catalog
-
-Google Cloud Data Catalog is a fully managed data discovery and metadata management service. Data Catalog can catalog the native metadata on data assets. Data Catalog is serverless, and provides a central catalog to capture both technical metadata as well as business metadata in a structured format.
-
-### About Teradata Vantage
-
-Vantage is the modern cloud platform that unifies data warehouses, data lakes, and analytics into a single connected ecosystem.
-
-Vantage combines descriptive, predictive, prescriptive analytics, autonomous decision-making, ML functions, and visualization tools into a unified, integrated platform that uncovers real-time business intelligence at scale, no matter where the data resides.
-
-Vantage enables companies to start small and elastically scale compute or storage, paying only for what they use, harnessing low-cost object stores and integrating their analytic workloads.
-
-Vantage supports R, Python, Teradata Studio, and any other SQL-based tools. You can deploy Vantage across public clouds, on-premises, on optimized or commodity infrastructure, or as-a-service.
-
-See the [documentation](https://docs.teradata.com/home) for more information on Teradata Vantage.
-
-### Prerequisites
-
-* Access to a Teradata Vantage instance.
-
-* A [**Google Service Account**](https://support.google.com/accounts/answer/27441?hl=en) with Data Catalog Admin role
-* A [**Cloud Console Project created**](https://cloud.google.com/resource-manager/docs/creating-managing-projects) for your account (i.e. partner-integration-lab)
-* Billing enabled
-* Google Cloud SDK [installed](https://cloud.google.com/sdk/docs/install) and [initialized](https://cloud.google.com/sdk/docs/initializing)
-* [Python](https://www.python.org/downloads/) installed
-* [Pip](https://pip.pypa.io/en/stable/installation/) installed
-
-### Procedure
-
-1. Enable Data Catalog APIs
-2. Install Teradata Data Catalog Connector
-3. Run
-4. Explore Teradata Vantage metadata with Data Catalog
-
-### Enable Data Catalog API
-
-* Logon to [Google](http://console.cloud.google.com/) console, choose **APIs & Services** from the Navigation menu, then click on _Library_. Make sure your project is selected on the top menu bar.
-
- 
-* Put _Data Catalog_ in the search box and click on **Google Cloud Data Catalog API**, click **ENABLE**
-
- 
-
-### Install Teradata Data Catalog Connector
-
-A Teradata Data Catalog connector is available on [GitHub](https://github.com/GoogleCloudPlatform/datacatalog-connectors-rdbms/tree/master/google-datacatalog-teradata-connector). This connector is written in Python.
-
-* Run following command to authorize gcloud to access the Cloud Platform with Google user credentials.
-
- ```bash
- gcloud auth login
- ```
-* Choose your Google account when the Google login page opens up and click _Allow_ on the next page.
-* Next, set up default project if you haven’t already done so
-
- ```bash
- gcloud config set project <project id>
- ```
-
-### Install virtualenv
-
-We recommend you install the Teradata Data Catalog Connector in an isolated Python environment. To do so, install [virtualenv](https://virtualenv.pypa.io/en/latest/) first:
-
-* **Windows**
-
- Run in Powershell as Administrator:
- ``` bash
- pip install virtualenv
- virtualenv --python python3.6 <your-env>
- <your-env>\Scripts\activate
- ```
-* **MacOS**
-
- ``` bash , role="content-editable emits-gtm-events"
- pip install virtualenv
- virtualenv --python python3.6 <your-env>
- source <your-env>/bin/activate
- ```
-* **Linux**
-
- ``` bash , role="content-editable emits-gtm-events"
- pip install virtualenv
- virtualenv --python python3.6 <your-env>
- source <your-env>/bin/activate
- ```
-
-#### Install Data Catalog Teradata Connector
-
-* **Windows**
-
- ``` bash
- pip.exe install google-datacatalog-teradata-connector
- ```
-* **MacOS**
-
- ``` bash , role="content-editable emits-gtm-events"
- pip install google-datacatalog-teradata-connector
- ```
-* **Linux**
-
- ``` bash , role="content-editable emits-gtm-events"
- pip install google-datacatalog-teradata-connector
- ```
-
-### Set environment variables
-
-``` bash , id="gcp_env_var", role="content-editable emits-gtm-events"
-export GOOGLE_APPLICATION_CREDENTIALS=<google_credentials_file>
-export TERADATA2DC_DATACATALOG_PROJECT_ID=<google_cloud_project_id>
-export TERADATA2DC_DATACATALOG_LOCATION_ID=<google_cloud_location_id>
-export TERADATA2DC_TERADATA_SERVER=<teradata_server>
-export TERADATA2DC_TERADATA_USERNAME=<teradata_username>
-export TERADATA2DC_TERADATA_PASSWORD=<teradata_password>
-```
-
-Where `` is the key for your service account (json file).
-
-### Run
-
-Execute `google-datacatalog-teradata-connector` command to establish entry point to Vantage database.
-
-``` bash , id="gcp_data_catalog_first_run" role="content-editable emits-gtm-events"
-google-datacatalog-teradata-connector \
- --datacatalog-project-id=$TERADATA2DC_DATACATALOG_PROJECT_ID \
- --datacatalog-location-id=$TERADATA2DC_DATACATALOG_LOCATION_ID \
- --teradata-host=$TERADATA2DC_TERADATA_SERVER \
- --teradata-user=$TERADATA2DC_TERADATA_USERNAME \
- --teradata-pass=$TERADATA2DC_TERADATA_PASSWORD
-```
-
-Sample output from the google-datacatalog-teradata-connector command:
-
-```
-INFO:root:
-==============Starting CLI===============
-INFO:root:This SQL connector does not implement the user defined datacatalog-entry-resource-url-prefix
-INFO:root:This SQL connector uses the default entry resoure URL
-
-============Start teradata-to-datacatalog===========
-
-==============Scrape metadata===============
-INFO:root:Scrapping metadata from connection_args
-
-1 table containers ready to be ingested...
-
-==============Prepare metadata===============
-
---> database: Gcpuser
-37 tables ready to be ingested...
-
-==============Ingest metadata===============
-
-DEBUG:google.auth._default:Checking /Users/Teradata/Apps/Cloud/GCP/teradata2dc-credentials.json for explicit credentials as part of auth process...
-INFO:root:Starting to clean up the catalog...
-DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token
-DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): oauth2.googleapis.com:443
-DEBUG:urllib3.connectionpool:https://oauth2.googleapis.com:443 "POST /token HTTP/1.1" 200 None
-INFO:root:0 entries that match the search query exist in Data Catalog!
-INFO:root:Looking for entries to be deleted...
-INFO:root:0 entries will be deleted.
-
-Starting to ingest custom metadata...
-
-DEBUG:google.auth._default:Checking /Users/Teradata/Apps/Cloud/GCP/teradata2dc-credentials.json for explicit credentials as part of auth process...
-INFO:root:Starting the ingestion flow...
-DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token
-DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): oauth2.googleapis.com:443
-DEBUG:urllib3.connectionpool:https://oauth2.googleapis.com:443 "POST /token HTTP/1.1" 200 None
-INFO:root:Tag Template created: projects/partner-integration-lab/locations/us-west1/tagTemplates/teradata_database_metadata
-INFO:root:Tag Template created: projects/partner-integration-lab/locations/us-west1/tagTemplates/teradata_table_metadata
-INFO:root:Tag Template created: projects/partner-integration-lab/locations/us-west1/tagTemplates/teradata_column_metadata
-INFO:root:Entry Group created: projects/partner-integration-lab/locations/us-west1/entryGroups/teradata
-INFO:root:1/38
-INFO:root:Entry does not exist: projects/partner-integration-lab/locations/us-west1/entryGroups/teradata/entries/gcpuser
-INFO:root:Entry created: projects/partner-integration-lab/locations/us-west1/entryGroups/teradata/entries/gcpuser
-INFO:root: ^ [database] 34.105.107.155/gcpuser
-INFO:root:Starting the upsert tags step
-INFO:root:Processing Tag from Template: projects/partner-integration-lab/locations/us-west1/tagTemplates/teradata_database_metadata ...
-INFO:root:Tag created: projects/partner-integration-lab/locations/us-west1/entryGroups/teradata/entries/gcpuser/tags/CWHNiGQeQmPT
-INFO:root:2/38
-INFO:root:Entry does not exist: projects/partner-integration-lab/locations/us-west1/entryGroups/teradata/entries/gcpuser_Categories
-INFO:root:Entry created: projects/partner-integration-lab/locations/us-west1/entryGroups/teradata/entries/gcpuser_Categories
-INFO:root: ^ [table] 34.105.107.155/gcpuser/Categories
-INFO:root:Starting the upsert tags step
-INFO:root:Processing Tag from Template: projects/partner-integration-lab/locations/us-west1/tagTemplates/teradata_table_metadata ...
-INFO:root:Tag created: projects/partner-integration-lab/locations/us-west1/entryGroups/teradata/entries/gcpuser_Categories/tags/Ceij5G9t915o
-INFO:root:38/38
-INFO:root:Entry does not exist: projects/partner-integration-lab/locations/us-west1/entryGroups/teradata/entries/gcpuser_tablesv_instantiated_latest
-INFO:root:Entry created: projects/partner-integration-lab/locations/us-west1/entryGroups/teradata/entries/gcpuser_tablesv_instantiated_latest
-INFO:root: ^ [table] 34.105.107.155/gcpuser/tablesv_instantiated_latest
-INFO:root:Starting the upsert tags step
-INFO:root:Processing Tag from Template: projects/partner-integration-lab/locations/us-west1/tagTemplates/teradata_table_metadata ...
-INFO:root:Tag created: projects/partner-integration-lab/locations/us-west1/entryGroups/teradata/entries/gcpuser_tablesv_instantiated_latest/tags/Ceij5G9t915o
-INFO:root:
-============End teradata-to-datacatalog============
-```
-
-### Explore Teradata Vantage metadata with Data Catalog
-
-* Go to [Data Catalog](https://console.cloud.google.com/datacatalog) console, click on the project (i.e. partner-integration-lab) under **Projects**. The Teradata tables are showing on the right panel.
-
- 
-* Click on the table to your interest (i.e. CITY_LEVEL_TRANS), and you’ll see the metadata about this table:
-
- 
-
-### Cleanup (optional)
-
-* Clean up metadata from Data Catalog. To do that, copy https://github.com/GoogleCloudPlatform/datacatalog-connectors-rdbms/blob/master/google-datacatalog-teradata-connector/tools/cleanup_datacatalog.py to local directory.
-* Change directory to where the file is and then run following command:
-
- ``` bash , role="content-editable emits-gtm-events"
- python cleanup_datacatalog.py --datacatalog-project-ids=$TERADATA2DC_DATACATALOG_PROJECT_ID
- ```