Boosting Productivity: Leveraging Cloud Data Warehouse AI Functions in Sigma for Enhanced Insights
Blog post from Sigma
Cloud data warehouses like Snowflake and Databricks have begun supporting large language models (LLMs) due to their ability to improve data processing and analysis by enabling natural language interactions and providing advanced insights. Sigma leverages these capabilities to enhance data analysis and visualization, making it more intuitive for users. Snowflake Cortex offers industry-leading LLMs like Mistral AI, allowing teams to focus on AI application development while Snowflake manages model optimization and infrastructure. Databricks provides built-in AI SQL functions for tasks such as sentiment analysis and translation, and Sigma's integration with these AI functions allows users to create custom, reusable functions for deeper insights. Best practices include data preparation, row filtering to minimize costs, and workbook materialization for efficient LLM result storage. Cost monitoring is vital, with Snowflake and Databricks offering tools to track AI service expenses. The integration of AI functions within these platforms offers scalable, secure, and user-friendly solutions, empowering organizations to innovate and drive growth in data analytics.