Teradata takes on Snowflake and Databricks with cloud-native platform

Giant for database analysis Teradata announced cloud-native database and analytics support. Teradata already had a cloud offering that ran on top of its infrastructure-as-a-service (IaaS) infrastructure, allowing businesses to run workloads on cloud and on-premise servers. The new service supports SaaS (Software-as-a-Service) deployment models that allow Teradata to compete with companies such as 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) workloads in the cloud. The combination of cloud-native database and analytics promises to streamline data science workflows, support ModelOps, and improve reuse from a single platform.

Teradata was a early leader in advanced data analytics capabilities that emerged from 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 delivered superior cost and performance characteristics compared to other approaches that required larger 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 latest offering is the culmination of Teradata’s three-year journey to create a new paradigm for analytics, one where superior performance, flexibility and value go hand in hand to provide insight for every level of an organization,” said Hillary Ashton. , chief product officer of Teradata.

Cloud native competition

Teradata’s first cloud offering ran on specially configured servers on a cloud infrastructure. This allowed enterprises to scale applications and data across on-premise and cloud servers. However, the data and analytics are scaled at the server level. If a company needed more computing power 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 a more dynamic infrastructure. Companies leveraged the latest cloud tooling to roll out new analytics at high speed. For example, Capital One rolled out 450 new analytics use cases following its move to Snowflake.

While these cloud-native competitors improved many aspects of scalability and flexibility, they lacked some aspects of governance and financial controls ingrained in legacy platforms. For example, after moving to the cloud, Capital One needed to develop an internal governance and management layer to enforce cost control. Capital One also created a framework to streamline the user analytics journey by incorporating content management, project management, and communications into a single tool.

Old meets new

This is where the new Teradata offerings promise to shine. It promises to combine the new types of architectures developed by cloud-native startups with the governance, cost control 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 an important and growing problem, and scale can play a role in managing workloads,” said Ashton.

The new offering also leverages Teradata’s distinct R&D in smart scaling, allowing users to scale based on actual resource usage rather than simple static statistics. The new offering also promises a lower total cost of ownership and immediate support for more types of analytics processing. For example, ClearScape Analytics includes query structure, 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 analysis lifecycle, from data transformation and statistical hypothesis testing to feature engineering and machine learning modeling. These capabilities are built right into the database, improving performance and eliminating the need to move data. This can help reduce the cost and friction of analyzing a large amount of data from millions of product sales or IoT sensors. Data scientists can code analytics functions into out-of-the-box 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 to production, Ashton said. New ModelOps capabilities include support for dataset auditing, code tracking, model approval workflows, model performance monitoring, and alerts when models become non-performing. This can help teams plan for retraining models when they begin to lose accuracy or exhibit bias.

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

Ultimately, it’s up to the market to decide whether these new capabilities will allow the legacy data pioneer to keep pace or even gain an edge over new cloud data startups.