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Data engineering workloads evolve as Snowflake redefines its value stack: theCUBE keynote analysis
- Enterprises are modernizing data stacks for AI-driven operations with open architectures, flexible compute models, and smarter orchestration.
- Snowflake is evolving to support more open, flexible, and cost-effective data engineering workloads.
- Snowflake's recent moves reflect a shift towards modular control over data engineering and analytics environments.
- The company is decoupling compute from storage and data, aligning with the needs of cost-conscious enterprises.
- New pricing models address past limitations, giving customers serverless options and the ability to bring their own compute.
- Data lineage graph importance is growing as workloads diversify across proprietary and open formats.
- Snowflake aims to support analytics and data science workloads across internal and external tables with advancements in catalog permissions.
- Snowflake's evolution is not just in response to competitors but a strategic move to maintain premium value in digital twins and agentic systems.
- By synchronizing with Polaris, Snowflake's engine competes effectively on open iceberg tables and attracts net new workloads, influencing their financial performance.
- TheCUBE is a paid media partner for Snowflake Summit, and their coverage provides deep insights into the evolving data engineering landscape.
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