cloudwithgoogle

At Google Cloud, our goal is to let you run large-scale analytical and data science workloads with maximum efficiency so you can process big data pipelines, machine learning, and ETL tasks. 

We recently announced that the Dataproc service is now

While building AI agents locally using Google’s Agent Development Kit (ADK) is an excellent way to prototype, production-ready agents require a robust, scalable infrastructure. For developers looking to move beyond simple instances and into the world of managed container orchestration, Google Kubernetes Engine (GKE) Autopilot offers the perfect balance of flexibility and ease...

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

Google Cloud Storage (GCS) is a foundational component of the modern agentic tech stack and the preferred home for unstructured data at scale. As enterprises deploy agents in production, the critical focus has shifted to turning data into context and building secure, standardized integrations to access context. This is the core of smart storage: making unstructured data inher...

Many data engineers spend significant time managing compatibility and getting best performance across multiple analytics engines. To help solve this pain point, we are excited to announce gcs-analytics-core, a new open-source Java library designed to centralize ...