Get quickly up to speed with Teradata Vantage. Learn about features. Find how-tos for common tasks. Explore sample source code.
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VantageCloud Lake Documentation
The complete cloud analytics and data platform, now with next-generation, cloud-native deployment and expanded analytics capabilities.
The right tools for the job
An extensive set Developers Tools for managing your development and integration with Vantage, such as IDEs, utilities and much more.
Drivers & Connectors
Connect your application to Vantage using any of the many popular programming development technologies such as Java, .Net, Python, Go.
Libraries and packages for your favorite opensource technologies such as Python and R, that take advantage of the Big Data and Machine Learning analytics capabilities of Teradata Vantage.
Get started with Teradata
Query data stored in object storage
Native Object Storage (NOS) is a Vantage feature that allows you to query data stored in files in object storage such as AWS S3, Google GCS, Azure Blob or on-prem implementations. It’s useful in scenarios where you want to explore data without building a data pipeline to bring it into Vantage.
Run large bulkloads efficiently with Teradata Parallel Transporter (TPT)
We often have a need to move large volumes of data into Vantage. Teradata offers Teradata Parallel Transporter (TPT) utility that can efficiently load large amounts of data into Teradata Vantage. This how-to demonstrates how to use TPT. In this scenario, we will load over 300k records, over 40MB of data, in a couple of seconds.
dbt with Teradata Vantage
This tutorial demonstrates how to use dbt (Data Build Tool) with Teradata Vantage. It’s based on the original dbt Jaffle Shop tutorial. A couple of models have been adjusted to the SQL dialect supported by Vantage.
Connect to Vantage using JDBC
This how-to demonstrates how to connect to Teradata Vantage using JDBC using a sample Java application: https://github.com/Teradata/jdbc-sample-app.
Train ML models in Vantage
There are situations when you want to quickly validate a machine learning model idea. You have a model type in mind. You don’t want to operationalize with an ML pipeline just yet. You just want to test out if the relationship you had in mind exists. Also, sometimes even your production deployment doesn’t require constant relearning with MLops. In such cases, you can use Vantage Analytics Library (VAL) and multiple ML model types it supports.
Teradata Query Service is a REST API for Vantage that you can use to run standard SQL statements without managing client-side drivers.
This how-to demonstrates how to create a connection to Teradata Vantage with DBeaver.
Go through connecting to Teradata Vantage from a Jupyter notebook.
New Guide - A Walk through on the Teradata Python Connector to get going, and explore the basic operations you can do, and other tools available.
Create a new project in ModelOps, upload the required data to Vantage, and track the full lifecycle of an imported Diabetes demo model using BYOM mechanisms.
The JDBC Driver allows you to connect to the Teradata database from external applications.