Systems | Development | Analytics | API | Testing

Accurate forecasting of cloud costs remains a significant challenge for 80% of data management experts (Forrester). The root causes? Lack of granular visibility, siloed data, and the absence of AI-powered predictive tools. Join us for this session in our Weekly Walkthrough drop-in series, "Controlling Cloud Costs," where we'll explore how to manage Snowflake costs with AI.

On this episode of The AI Forecast, Mike Walsh talks us through what he calls the fifth and latest industrial revolution - and that’s AI. Mike is a futurist and expert in disruptive technologies. He’s a best-selling author, host of the Between Worlds podcast, and CEO of Tomorrow – a global consultancy helping organizations adapt to the challenges of the digital age.

Apache Calcite Hybrid Meetup 20th Feb'24 5:00PM to 8:00PM PST Cloudera Santa Clara Office.

Learn how Snowflake is expanding its partnership with Microsoft to bring OpenAI’s state-of-the-art models to Snowflake Cortex AI.

Over the past few years, Spark New Zealand has tackled the challenge of creating a strong data foundation by moving all of its data warehouses into Snowflake to create a centralised data platform. Now, explains Pritha Chattopadhyay, Domain Chapter Lead at Spark, this telecommunications leader and digital services provider is diving into artificial intelligence with the help of Snowflake Cortex AI. Tune in to learn about the benefits it provides.

Accurate forecasting of cloud costs remains a significant challenge for 80% of data management experts (Forrester). The root causes? Lack of granular visibility, siloed data, and the absence of AI-powered predictive tools. Join us for this session in our Weekly Walkthrough drop-in series, "Controlling Cloud Costs," where we'll explore how to manage Databricks costs with AI.

This demo showcases Cloudera Data Engineering's External IDE Connectivity, powered by the Spark Connect API. Data engineers can connect local IDEs like VS Code or Jupyter Lab to cloud clusters, run Spark workflows remotely, and sync code with GitHub. This feature enhances data engineering flexibility and productivity by streamlining CI/CD pipelines and enabling seamless synchronization between local and cloud environments, empowering downstream multi-functional analytics and AI.