Teradata takes on Snowflake and Databricks with cloud-native platform

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Database analytics giant Teradata has announced cloud-native database and analytics support. Teradata already has cloud offerings running on top of an infrastructure as a service (IaaS) infrastructure, enabling enterprises to run workloads across the cloud and on-premises servers. The new service supports a software-as-a-service (SaaS) deployment model that will help Teradata compete with companies such as Snowflake and Databricks.

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

Teradata is early leader in advanced data analytics capabilities that grew out of the collaboration between the California Institute of Technology and Citibank in the late 1970s. The company optimizes techniques to scale analytics workloads across multiple servers running in parallel. Scaling between servers provides superior cost and performance properties compared to other approaches that require larger servers. The company launched 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, where superior performance, agility and value all go hand in hand to provide insight for every level of the organization,” said Hillary Ashton, Teradata’s chief product officer.


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Cloud-native competition

Teradata’s first cloud offering runs on specially configured servers on the cloud infrastructure. This allows enterprises to scale applications and data across on-premises and cloud servers. However, data and analytics are scaled at the server level. If a company needs more computing or storage, it must provide more servers.

This opens up opportunities for new cloud storage startups like Snowflake to leverage new architectures built on container, mesh, and orchestration techniques for more dynamic infrastructure. Companies leverage the latest cloud tools to roll out new analytics at high speed. For example, Capital One launched 450 new analytics use cases after moving to Snowflake.

While these cloud-native competitors improve many aspects of scalability and flexibility, they lack some of the governance and financial control aspects incorporated into the legacy platform. For example, after Capital One moved to the cloud, it had to develop internal governance and management levels to enforce cost controls. Capital One also created a framework to streamline the user analytics journey by combining content management, project management and communications in one tool.

Old meet new

This is where the new Teradata offering promises to shine. It promises to combine a new type of architecture pioneered by cloud-native startups with the governance, cost control, and simplicity of a consolidated offering.

“Snowflake and Databricks are no longer the only answer to smaller data and analytics workloads, especially in larger organizations where shadow systems are a significant and growing issue, and scale can play a role in workload management issues,” said Ashton. .

This new offering also leverages Teradata’s various R&D into intelligent scaling, allowing users to scale based on actual resource utilization rather than simple static metrics. This new offering also promises lower total cost of ownership and direct support for more types of analytical processing. For example, ClearScape Analytics includes query structure, governance, and financial visibility. It also promises to simplify predictive and prescriptive analysis.

ClearScape Analytics includes time series functionality in the database that simplifies the entire analytics 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 large volumes of data from millions of sales of IoT products or sensors. Data scientists can code analytics functions into built-in components that can be reused by other analytics, machine learning, or AI workloads. For example, manufacturers can build anomaly detection algorithms 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 auditing data sets, code tracking, model approval workflows, monitoring model performance, and alerting when models become underperforming. This can help teams schedule model retraining when they start to lose accuracy or show bias.

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

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

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