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Teradata launches cloud-native platform, enhances BI suite

VantageCloud Lake is designed to enable wider use across organizations along with better cost control, while ClearScape Analytics adds more than 50 new capabilities.

Teradata on Monday launched VantageCloud Lake, which is a new cloud-native version of its data and analytics platform, as well as ClearScape Analytics, an expanded -- and rebranded -- version of its business intelligence suite.

Both were unveiled during an event at the New York Stock Exchange and are now generally available.

Founded in 1979 and based in San Diego, Teradata is a data and analytics vendor offering both data management and analytics capabilities.

Before the introduction of VantageCloud Lake, Teradata's platform for data and analytics was known as Vantage. That platform, which has now been renamed VantageCloud Enterprise, was built to be cloud-first but not cloud-only, has multi-cloud capabilities and is designed and priced for use by IT departments at enterprise scale.

Vantage was first released in January 2019, and most recently was updated in June to include an integration with Amazon SageMaker.

New platform

Unlike VantageCloud Enterprise, VantageCloud Lake was built on an all-new cloud-native architecture and is priced and designed for more self-service use at the department level within organizations. The new product enables ad hoc and exploratory queries, and it can be turned on and off to minimize expenses.

While specific pricing details are not publicly available, the offering is available for less than $50,000 annually with a yearly contract -- which is substantially less than VantageCloud Enterprise -- according to Teradata.

Motivation for the development of VantageCloud Lake came from a combination of Teradata's own interest in taking better advantage of a cloud-native architecture and conversations the vendor has had with its customers, according to Hillary Ashton, Teradata's chief product officer.

"It was a little bit of both," she said. "We started this journey a couple of years ago feeling that there were capabilities in the cloud that we wanted to take better advantage of, but [the development] has really been shaped by our customer discussions and experiences."

In particular, discussions with customers led Teradata to realize there were potential users that wanted to build augmented intelligence and machine learning models but didn't have the budget to use the vendor's platform, Ashton continued.

That, in part, led to the development of a version that could be used for less than $5,000 per month.

"We realized that being able to make smaller systems that could scale up and down really quickly was as much of an advantage, if not more of an advantage, from an expansion perspective than being able to [add customers] at the enterprise level," Ashton said.

Now, rather than deploy Teradata's tools at an enterprise level, organizations are able to use the vendor's tools at a departmental level.

In addition, VantageCloud Lake enables organizations to better allocate data and analytics resources to meet specific needs, maintain better governance and cost control over their Teradata deployments, and eliminate the need for centralized data teams to do the ad hoc analysis and exploratory projects now enabled by self-service access.

This is a big deal for Teradata, not only because they have invested so much in these efforts but because they have finally made the transition from being the heaviest of heavyweight enterprise data platforms to being a more agile, easily adopted offering suitable for all kinds of businesses.
Donald FarmerFounder and principal, TreeHive Strategy

That evolution away from the need to go through data teams and IT departments, meanwhile, is key for Teradata, according to Donald Farmer, founder and principal of TreeHive Strategy.

"This is a big deal for Teradata, not only because they have invested so much in these efforts but because they have finally made the transition from being the heaviest of heavyweight enterprise data platforms to being a more agile, easily adopted offering suitable for all kinds of businesses," he said.

The release of VantageCloud Lake is also a big deal because it brings Teradata's database capabilities in line with those of the most advanced database vendors such as Databricks, SingleStore and Snowflake, and tech giants AWS, Google and Microsoft, Farmer continued.

Meanwhile, its pricing and governance serve as differentiators.

"Teradata is bringing something unique to the cloud database market -- a genuinely mission-critical, high-performance solution capable of complex mixed-workloads," he said. "Teradata ... is the real deal, including significant features such as workload management and cost governance, which are both areas where competitors are not just behind but notably weak."

Following the release of VantageCloud Lake, where Teradata needs to improve is in the area of perception, according to Farmer.

Teradata's data management and storage capabilities don't lack functionality compared to those of its competitors, but its tools have historically been aimed at data and analytics experts rather than self-service users. And deployments have been enterprise scale rather than incremental.

Therefore, Teradata needs to alter the way it's viewed by potential customers.

"I don't think Teradata has overlooked anything, but only experience will tell if they have that important onboarding process right," Farmer said. "The perception in the market will still be that Teradata is a beast. They need to embrace their cuddly side and seem less scary."

VantageCloud Lake is built to work with multiple clouds and currently available on AWS with plans to add Azure and Google Cloud next, according to Ashton. But because Teradata views Databricks and Snowflake as direct competitors, only connectors are available to data stored in those platforms.

"We're AWS out the door primarily because of where we've seen demand in the market from our customers," Ashton said. "We look at AWS, Azure and Google in that order, so we are looking at a fast follow with Azure."

Upgraded analytics

In addition to the launch of VantageCloud Lake, Teradata introduced ClearScape Analytics, an expanded version of its analytics platform that enables users to work with their data in-database rather than force them to extract, transform and load data before exploration and analysis.

Highlights include more than 50 time series functions that enable organizations to do forecasting and tools to support machine learning, according to Ashton.

In addition, ClearScape Analytics includes new model operations capabilities designed to make it easier to implement AI and machine learning models, many of which historically never make it into production; the ability to work with multiple coding languages such as R and Python; and a feature store where users can share their work and collaborate.

"Time series forecasting is a really powerful analytics tool," Ashton said. "It's added to our list of analytics capabilities."

Likewise, Farmer noted that the addition of nearly 50 time series functions has the potential to substantially benefit users.

"ClearScape Analytics' new functions for time series analysis are very impressive," he said. "Whether analyzing IoT data, performing complex predictions or reporting over complex calendars, they have functions for that [which are capable of] running in the database for maximum performance."

The new ModelOps capabilities are also significant, Farmer added.

"In too many enterprises, excellent data science is difficult to deploy because models which look great on the workbench can be tricky or expensive to run in practice," he said. "The in-database capabilities and the model management features make ClearScape a very compelling offering."

Despite the tandem launches of VantageCloud Lake and ClearScape Analytics, Teradata has plans to enhance and improve its capabilities to address what gaps remain.

Unlocking data remains a challenge for many organizations, so one focus of Teradata's roadmap is to add tools that make it easier to find the most relevant data and make it operational, according to Ashton. In addition, areas of focus include providing better cost efficiency and adding more AI and machine learning capabilities such as improving analysis with the use of digital twins.

"We want to take our AI and ML capabilities to that next level," Ashton said. "We have customers today who are doing digital twin work with our streaming capabilities, and we think there's a big future for that use case."

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