Highlights:

• With Teradata ClearScape, enterprises can take advantage of emerging analytics, machine learning, and AI development workloads in the cloud.
• Within a single platform, the combination of cloud-native database and analytics has the potential to increase reuse, facilitate ModelOps, and expedite data science workflows.

Teradata, a database analytics giant, announced cloud-native database and analytics support. Thanks to its cloud service that runs on top of infrastructure-as-a-service (IaaS) servers, enterprises could run workloads across cloud and on-premise servers. The new service lends support to SaaS deployment patterns. With this, Teradata will be able to compete with businesses like Snowflake and Databricks better.

The firm is introducing two brand-new cloud-native products and service offerings. VantageCloud Lake expands the Teradata Vantage data lake to a more elastic cloud deployment strategy. With Teradata ClearScape, enterprises can take advantage of emerging analytics, machine learning, and Artificial Intelligence (AI) development workloads in the cloud. A combination of cloud-native database and analytics holds the potential to increase reuse, facilitate ModelOps, and expedite data science workflows within a single platform.

A collaboration between the California Institute of Technology and Citibank in the late 1970s gave rise to Teradata, an early pioneer in sophisticated data analytics skills. The organization developed approaches to scale analytics workloads over numerous servers in parallel. Scaling across servers yielded improved cost and performance compared to other strategies that needed larger servers. In 2011, the business launched data warehousing and analytics as a service with the release of the Teradata Vantage linked multi-cloud data platform.

Hillary Ashton, chief product officer of Teradata, said, “Our newest offerings are the culmination of Teradata’s three-year journey to create a new paradigm for analytics, one where superior performance, agility and value all go hand-in-hand to provide insight for every level of an organization.”

Cloud-native competition

The original cloud products from Teradata operated on specially designed cloud infrastructure servers. This enabled businesses to expand data and applications across on-premise and cloud servers. The data and analytics, however, grew at the server level. If a company needed more computing or storage, it had to deploy additional servers.

This opened the door for new cloud data storage firms like Snowflake to leverage new designs based on containers, meshes, and orchestration approaches for a more dynamic infrastructure. Utilizing the most recent cloud-based tools, businesses rolled out new insights rapidly. Capital One, for instance, built nearly 450 new analytics use cases after using Snowflake.

Although these cloud-native rivals enhanced many scalability and flexibility features, they lacked specific governance and financial controls inherent to traditional systems. Capital One, for instance, had to establish an internal power and management tier to impose cost constraints after migrating to the cloud. Capital One also designed a framework to simplify the user analytics experience by combining content management, project management, and communication into a single application.

Old meets new

It is here that new Teradata services aim to excel. It promises to integrate innovative cloud-native startup designs with a consolidated service’s governance, cost constraints, and simplicity.

Hillary Ashton said, “Snowflake and Databricks are no longer the only answer for smaller data and analytics workloads, especially in larger organizations where shadow systems are a significant and growing issue, and scale may play into workloads management concerns.”

The new service also leverages Teradata’s research into smart scaling, allowing users to scale based on actual resource consumption instead of basic static measurements. Additionally, the new product offers a cheaper total cost of ownership and direct support for various analytics processing types. ClearScape Analytics, for instance, features a query framework, governance, and financial visibility. Additionally, this is aimed at simplifying predictive and prescriptive analytics.

ClearScape Analytics includes in-database time series functions that facilitate the complete analytics lifecycle, from data processing and statistical hypothesis testing to feature engineering and machine learning modeling. These features are included directly into the database, enhancing performance and removing the need to transfer data. This can lessen the cost and difficulty of evaluating enormous volumes of data from millions of product sales or Internet of Things sensors. Data scientists may codify analytics functions as prefabricated components that can be utilized by other analytics, machine learning, and AI workloads. For instance, a manufacturer may develop an algorithm for anomaly identification to enhance predictive maintenance.

Predictive models need a significant amount of exploratory investigation and testing. Ashton stated that despite the investment in tools and labor, most predictive models never make it into production. New ModelOps capabilities include support for auditing datasets, code tracking, procedures for model approval, monitoring model performance, and alerts when models become non-performing. This can assist teams in scheduling model retraining when they begin to lose precision or exhibit bias.

Ashton further said, “What sets Teradata apart is that it can serve as a one-stop shop for enterprise-grade analytics, meaning companies don’t have to move their data. They can simply deploy and operationalize advanced analytics at scale via one platform.”