Serverless Managed Service for Apache Spark runtime 3.0 features
Whether you use it for data preparation, real-time interactive queries, AI model training, or something entirely different, running Apache Spark at scale is demanding — you shouldn’t have to manage the underlying infrastructure too.
Late last year, we announced the general availability (GA) of our serverless Managed Service for Apache Spark runtime version 3.0, prioritizing speed, simplicity, and reliability. Since then, customer use of Managed Service for Apache Spark for data science has nearly doubled year over year. This is a testament to our belief that using Google Cloud is the easier, smarter, and faster place to run your Apache Spark workloads.
In this blog, let’s dive into a few key features that make our serverless Apache Spark offering a great fit for a wide range of workflows, including feature engineering, GPU-accelerated model training and tuning, semantic search, RAG, building AI agents and applications, and more.
Zero-setup onboarding
The most...
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