Google Cloud Platform
We build exclusively on GCP — not because it's what we know, but because it's the right choice for European data teams that need compliance, global reach, and a best-in-class analytics stack.
What It Is
Google Cloud Platform is Google's suite of cloud computing services. For data work, it offers an integrated stack — from storage (Cloud Storage, BigQuery) to transformation (Dataform, Dataproc) to visualization (Looker) to AI (Vertex AI, Gemini) — all designed to work together without custom glue code.
Why We Chose It
GCP's analytics stack is the most cohesive in the market. BigQuery alone changes how you think about data — serverless, fast, and economical at the right scale. The tight integration between services means less time debugging infrastructure and more time building value.
For European clients specifically, GCP now offers three levels of data sovereignty — standard regional residency, Assured Workloads for EU (EU-only storage, processing, and support), and sovereign partnerships like S3NS (Thales + Google, SecNumCloud-aligned in France) — which matter when GDPR, regulated sectors, or procurement requirements enter the picture.
How We Use It
Design and provision GCP project architecture with proper IAM, VPC, and billing structure
Configure BigQuery datasets, access controls, and cost governance (slot reservations, column-level security)
Set up Cloud Run Functions and Cloud Scheduler for lightweight scheduled jobs and custom API integrations
Implement Cloud Storage lifecycle policies and data landing zones for ingestion pipelines
Advise on GCP region selection and sovereignty options for EU data residency (GDPR, Assured Workloads, S3NS)
When GCP is the right choice — and when it isn't
We're GCP-only, but that doesn't mean GCP is right for everyone.
Choose GCP when:
- Your primary workload is analytics (BigQuery is the best-value warehouse in the market)
- You need European data residency with clean EU multi-region support
- You're already in the Google ecosystem (Google Ads, GA4, Workspace)
- AI workloads (Gemini, Vertex AI) are part of your data roadmap
- You want a cohesive stack with fewer integration points
Choose AWS when:
- You're running complex multi-service applications on Lambda, SQS, DynamoDB, and the AWS-native stack
- Your data team is already deep in AWS infrastructure
- You need the broadest enterprise feature set available
Choose Azure when:
- Your organization is heavily Microsoft-invested (M365, Power BI, Active Directory)
- Data gravity is already on Azure and moving it would cost more than it saves