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Database analytics giant Teradata has announced cloud-native database and analytics support. Teradata already had a cloud offering that ran on top of infrastructure-as-a-service (IaaS) infrastructure, enabling enterprises to run workloads across cloud and on-premise servers. The new service supports software-as-a-service (SaaS) deployment models that will help Teradata compete against companies like Snowflake and Databricks.

The company is launching two new cloud-native offerings. VantageCloud Lake extends the Teradata Vantage data lake to a more elastic cloud deployment model. Teradata ClearScape Analytics helps enterprises take advantage of new analytics, machine learning and artificial intelligence (AI) development workloads in the cloud. The combination of cloud-native database and analytics promises to streamline data science workflows, support ModelOps and improve reuse from within a single platform. 

Teradata was an early leader in advanced data analytics capabilities that grew out of a collaboration between the California Institute of Technology and Citibank in the late 1970s. The company optimized techniques for scaling analytics workloads across multiple servers running in parallel. Scaling across servers provided superior cost and performance properties compared to other approaches that required bigger servers. The company rolled out data warehousing and analytics on an as-a-service basis in 2011 with the introduction of the Teradata Vantage connected multicloud data platform.

“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,” said Hillary Ashton, chief product officer of Teradata.

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Cloud-native competition

Teradata’s first cloud offerings ran on specially configured servers on cloud infrastructure. This allowed enterprises to scale applications and data across on-premise and cloud servers. However, the data and analytics scaled at the server level. If an enterprise needed more compute or storage, it had to provision more servers. 

This created an opening for new cloud data storage startups like Snowflake to take advantage of new architectures built on containers, meshes and orchestration techniques for more dynamic infrastructure. Enterprises took advantage of the latest cloud tooling to roll out new analytics at high speed. For example, Capital One rolled out 450 new analytics use cases after moving to Snowflake

Although these cloud-native competitors improved many aspects of scalability and flexibility, they lacked some aspects of governance and financial controls baked into legacy platforms. For example, after Capital One moved to the cloud, it had to develop an internal governance and management tier to enforce cost controls. Capital One also created a framework to streamline the user analytics journey by incorporating content management, project management and communication within a single tool. 

Old meets new

This is where the new Teradata offerings promise to shine. It promises to combine the new kinds of architectures pioneered by cloud-native startups with the governance, cost-controls and simplicity of a consolidated offering. 

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,” Ashton said. 

The new offering also takes advantage of Teradata’s various R&D into smart scaling, allowing users to scale based on actual resource utilization rather than simple static metrics. The new offering also promises a lower total cost of ownership and direct support for more kinds of analytics processing. For example, ClearScape Analytics includes a query fabric, governance and financial visibility. This also promises to simplify predictive and prescriptive analytics. 

ClearScape Analytics includes in-database time series functions that streamline the entire analytics lifecycle, from data transformation and statistical hypothesis tests to feature engineering and machine learning modeling. These capabilities are built directly into the database, improving performance and eliminating the need to move data. This can help reduce the cost and friction of analyzing a large volume of data from millions of product sales or IoT sensors. Data scientists can code analytics functions into prebuilt components that can be reused by other analytics, machine learning, or AI workloads. For example, a manufacturer could create an anomaly detection algorithm to improve predictive maintenance. 

Predictive models require more exploratory analysis and experimentation. Despite the investment in tools and time, most predictive models never make it into production, said Ashton. New ModelOps capabilities include support for auditing datasets, code tracking, model approval workflows, monitoring model performance and alerting when models become non-performing. This can help teams schedule model retraining when they start to lose accuracy or show bias.

“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,” Ashton said. “They can simply deploy and operationalize advanced analytics at scale via one platform.”

Ultimately, it is up to the market to decide if these new capabilities will allow the legacy data pioneer to keep pace or even gain an edge against new cloud data startups. 

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