Teradata Cloud: Elastic Compute — Scale Smarter as Workloads Change

Analytics pipelines, AI preparation, and ad hoc queries create changing compute demands that can be difficult to manage with fixed capacity. Provisioning for peak demand can leave resources idle, while insufficient capacity can delay critical workloads. This white paper explores how Teradata Cloud Elastic Compute adds isolated, on-demand capacity in smaller increments to better align resources with workload requirements. It explains how granular scaling, open data access, and managed workload operations can support new analytics and AI use cases while helping protect production performance. Data and infrastructure leaders will also find examples illustrating how elastic capacity can support scheduled processing and periodic model preparation.

Key takeaways:

• Learn how workload isolation helps reduce contention between variable demand and critical production services.
• Explore access to supported open table formats, including Iceberg and Delta, without disruptive platform migrations.
• Understand how scheduling, auto-suspend, and consumption visibility help manage resource use and cloud costs.

Download the white paper to learn how to align compute capacity with changing enterprise workloads.

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