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How Snowflake is Simplifying Data Engineering in 2025
- Sumeet Tandure, senior manager of sales engineering at Snowflake, discussed data engineering disruptions and principles at AIM’s DES 2025 event.
- Transitioning from experimentation to production requires a strong data engineering foundation to achieve ROI.
- LLMs are simplifying value extraction from unstructured data types like PDFs, audios, and videos.
- Snowflake's Document AI automates extraction of specific fields from documents, enhancing data engineers' capabilities.
- Data interoperability, exemplified by Iceberg, enables multiple engines to communicate regardless of origin.
- Iceberg's ecosystem is expanding to open-source catalogues, reducing vendor lock-in and enhancing interoperability.
- Snowflake's Cortex Search simplifies pipelines for building search engines and chatbots, boosting developer productivity.
- Snowflake provides governance tools like automatic classification of Indian identifiers and attribute-based access controls.
- Snowflake integrates DevOps principles into data operations with Python APIs, Snowflake CLI, and CI/CD workflows.
- Snowflake offers end-to-end data engineering capabilities with support for various integration options, transformations, and delivery methods.
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