Teradata, the connected multi-cloud data platform for enterprise analytics, is announcing its newfound collaboration with Microsoft, bringing the Teradata VantageCloud Lake to Microsoft Azure, extending the solution’s reach while simultaneously broadening its use cases to include generative AI.
The availability of VantageCloud Lake on Azure brings the numerous benefits of Teradata’s modern, cloud native architecture to Microsoft customers’ fingertips. Features such as full separation of compute and storage, and independent, elastic, and multi-cluster compute will be available to Microsoft users, further benefited by Azure Data LakeStorage.
For those looking to modernize workloads and applications, Teradata VantageCloud Lake is a worthy option; designed to be cost-effective and dynamically scalable with self-service provisioning, this solution brings harmonized data to enterprises at any step in their modernization journey, according to the vendor.
“With VantageCloud Lake on Azure, customers can migrate workloads in-tact, allowing for faster time-to-value with the ability to leverage the new cloud native architecture,” said Hillary Ashton, CPO at Teradata. “Then, they can modernize data pipelines and workload applications selectively over time, which is better because customers can dictate the pace of modernization based on their business needs.”
VantageCloud Lake on Azure also offers the following advantages:
- Minimized risk and accelerated time-to-value in cloud migration to Azure
- An exclusive high availability feature that leverages Azure to increase cloud availability
- Optimized system sizes for increasing overall uptime
On top of the myriad ways that Azure and VantageCloud Lake expand their power when combined, these collaborators offer extremely robust analytic capabilities for end-to-end analytic pipelines, driving beneficial outcomes in AI and machine learning (ML) use cases. With Teradata’s ClearScape Analytics and Microsoft Azure ML, enterprises can employ various data processes—including data preparation, model training, and operationalizing at scale—to implement widespread analytics throughout an organization.
In regard to generative AI, Teradata and Azure ML offer a unique combination of technology that empowers this popular use case to make the most of proprietary data. By utilizing Azure ML to train large language models (LLMs), these models can be brought into Teradata for real-time scoring. These models can then be scaled depending on enterprise needs, granting greater accessibility of generative AI to any organization.
“Companies want to drive innovation through new AI/ML use cases, but one of the toughest challenges is delivering results. It often takes too long to move analytics into production, if it happens at all,” explained Ashton. “Using data from VantageCloud Lake, combined with the power of ClearScape Analytics and Azure ML, allows companies to expand analytics across the enterprise and realize better outcomes.”
Despite the exciting promise of generative AI use cases, Ashton warned against becoming overzealous without evaluating an enterprise’s resources.
“Of course, even in these use cases, it’s crucial to not get lost in the novelty,” she said. “As exciting as LLMs and generative AI are right now, it’s always critical to start with the end goal in mind and consistently evaluate ROI and price performance. VantageCloud Lake on Azure has the financial governance to do that, even at enterprise scale.”
Teradata VantageCloud Lake on Azure with ClearScape Analytics is available worldwide, today. Additionally, VantageCloud Lake is available on AWS.
To learn more about Teradata VantageLake on Microsoft Azure, please visit https://www.teradata.com/ or https://azure.microsoft.com/en-us/homepage-b/.