Why data mesh
Data lakes, warehouses and lakehouses all rely on bringing the organisation’s data into one place, run by one central team. In practice that team becomes a bottleneck, and some data never makes it in at all.
Data mesh is a decentralised alternative. Domain teams own their data as products and publish them for others to use, under shared standards for quality, access and governance.
The Google Cloud building blocks
- BigQuery: serverless data warehouse for analytics and machine learning.
- Dataproc: managed Apache Spark and Hadoop for large-scale processing.
- Dataflow: managed stream and batch processing.
- Pub/Sub: messaging for data ingestion and distribution.
- Cloud Functions: serverless compute for event-driven pipeline steps.
What it changes
- Faster access: teams use trusted, domain-owned data without waiting on a central team.
- Clear ownership: every data product has an owner accountable for its quality and compliance.
- Room to experiment: teams can try new use cases on data they already understand.
- Scale: Google Cloud handles growing data volumes without a central platform rebuild.
Working with us
We design, build and run data mesh architectures on Google Cloud, starting from how your teams and data are organised today. Contact us to talk it through.
Talk to an architect who has done this before.
Bring your current setup and the outcome you need. You will get a view on the approach, the risks and roughly what it costs.
Straight to a senior GCP architect. No SDR, no slide deck.
Not ready to talk? See how we migrated Hapana off AWS →
Or call +61 2 8359 9507 · Hello@aviato.consulting