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 significant barrier to entry for a cloud service is often the “time to magic moment” — the interval between creating a project and running your first workload. Previously, with serverless Spark, you still needed to manually configure IAM roles, VPC networking, and firewall rules before submitting a single job.
In the serverless Spark 3.0 runtime version, zero-setup onboarding significantly reduces the time to launch your first workload on serverless Spark. It does so by automating the following steps:
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Permissions: Necessary IAM roles and permissions are automatically provisioned to the appropriate service accounts.
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Networking: Private Google Access is auto-enabled on subnets, and system firewall policies are configured automatically.
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API management: Enabling APIs is now more efficient; you can just enable the Managed Service for Apache Spark API instead of manually having to enable several different APIs, as you did previously.
Fast startup for SLA-sensitive workloads
Latency matters, especially for interactive data science and SLA-sensitive batch pipelines. Historically, serverless Spark startup times could take several minutes. With the 3.0 runtime, we’ve dropped startup times by 75% across both standard and premium tiers, delivered automatically without any code or configuration changes and at no additional cost.
This massive improvement qualifies serverless Spark for a much broader range of SLA-sensitive workloads, and we’re always looking to optimize startup times even further.
“Serverless Spark allowed us to quickly reap benefits by removing the need for fine-grain machine management. This drove faster model development and significantly reduced our data processing costs.” – César Narnajo, Principal Engineer, Moloco
