---
title: "Google Cloud Use Cases: How Enterprises Run Data & AI on GCP with Databricks"
description: "The guide presents Google Cloud as a unified platform for enterprise data engineering, analytics, machine learning, and generative AI, with managed services intended to reduce infrastructure and integration work. It focuses on practical architectures, including medallion ETL pipelines that store Bronze, Silver, and Gold Delta Lake tables in Google Cloud Storage and expose Gold data to BigQuery through an optimized connector. For streaming workloads, Cloud Pub/Sub supplies ingestion while Apache Spark Structured Streaming processes continuous micro-batches; together with Delta Lake ACID transactions, this supports exactly-once results despite duplicates or out-of-order messages. The text also describes Cloud Run and Cloud Functions for containerized or event-driven workloads, and identifies retrieval-augmented generation applications on Vertex AI as a common starting point for generative AI. It concludes that integrated service combinations, rather than isolated tools, are the central strategic advantage for organizations building evolving data and AI platforms on Google Cloud."
---

# Google Cloud Use Cases: How Enterprises Run Data & AI on GCP with Databricks

[Databricks](https://yomu.fyi/company/databricks) · Databricks Staff · Sep 18, 2026

**Type:** Explainer

## Summary

The guide presents Google Cloud as a unified platform for enterprise data engineering, analytics, machine learning, and generative AI, with managed services intended to reduce infrastructure and integration work. It focuses on practical architectures, including medallion ETL pipelines that store Bronze, Silver, and Gold Delta Lake tables in Google Cloud Storage and expose Gold data to BigQuery through an optimized connector. For streaming workloads, Cloud Pub/Sub supplies ingestion while Apache Spark Structured Streaming processes continuous micro-batches; together with Delta Lake ACID transactions, this supports exactly-once results despite duplicates or out-of-order messages. The text also describes Cloud Run and Cloud Functions for containerized or event-driven workloads, and identifies retrieval-augmented generation applications on Vertex AI as a common starting point for generative AI. It concludes that integrated service combinations, rather than isolated tools, are the central strategic advantage for organizations building evolving data and AI platforms on Google Cloud.

## Context

The material targets data engineers, data scientists, and executives evaluating or expanding Google Cloud investments. It covers data pipelines, streaming architectures, analytics, machine learning, generative AI, and privacy considerations for regulated industries.

## Approach / What changed

The described approach combines Google Cloud Storage, Delta Lake, BigQuery, Pub/Sub, Apache Spark Structured Streaming, Vertex AI, Cloud Run, and Cloud Functions into integrated data and AI architectures. It uses medallion layers for progressive data refinement, MERGE INTO for targeted updates, streaming micro-batches for low-latency processing, and retrieval-augmented generation as an initial generative AI pattern.

## Takeaways

- Medallion pipelines refine raw Bronze data into Silver and Gold Delta Lake tables, with Gold data written to BigQuery through an optimized connector for SQL-based analysis.
- Pub/Sub provides managed message ingestion, while Spark Structured Streaming handles continuous micro-batches; Delta Lake ACID transactions support correct, deduplicated stateful results.
- A retrieval-augmented generation application using proprietary documents and Vertex AI can provide a starting point for generative AI without model training.

## FAQ

### How does the medallion architecture reduce data-processing work?

It progressively transforms raw Bronze data into filtered, conformed Silver data and aggregated Gold data. MERGE INTO updates only affected rows when upstream records change, avoiding a full refresh and preserving an auditable history.

### How are real-time pipelines protected from duplicate or late messages?

Cloud Pub/Sub buffers streaming data with at-least-once delivery, while Apache Spark Structured Streaming processes continuous micro-batches. Combined with Delta Lake ACID guarantees, the architecture supports exactly-once processing for stateful operations despite duplicates or out-of-order delivery.

### What is a common first generative AI application on Google Cloud?

Organizations commonly start with retrieval-augmented generation, which retrieves relevant documents from a proprietary knowledge base and uses a Vertex AI foundation model to generate grounded responses. Existing documentation in Cloud Storage or Google Drive can be used without model training, and the application can run on Cloud Run.

**Tags:** [Databricks](https://yomu.fyi/topic/databricks), [Delta Lake](https://yomu.fyi/topic/delta-lake), [Google Cloud](https://yomu.fyi/topic/gcp), [Spark](https://yomu.fyi/topic/spark), [Vertex](https://yomu.fyi/topic/vertex)

- Source: [Databricks](https://www.databricks.com/blog/google-cloud-use-cases)
- Source URL: https://www.databricks.com/blog/google-cloud-use-cases
- Ingested by Yomu: 2026-09-17T14:13:03.034Z

[Read original post](https://www.databricks.com/blog/google-cloud-use-cases)