Turn Data Into Decisions on Google Cloud
BigQuery uncovers insights in seconds. Vertex AI builds models without the overhead. We architect the data pipeline that powers your business intelligence—from ingestion to insights.
What we deliver with Google Cloud
BigQuery data modelling
Layered datasets with partitioning and clustering chosen from real query patterns, so performance holds as volume grows instead of degrading quietly.
Pipeline engineering
Dataflow or Dataproc for movement, scheduled transformations for modelling, and data quality checks that block bad loads rather than reporting them later.
Cost governance on slots and storage
On-demand versus committed capacity chosen from measured usage, query budgets, table expiry policies, and a review of the queries that scan the most data.
Vertex AI and model operations
Feature definitions, training jobs, endpoint deployment, and drift monitoring, with the lineage an auditor will ask for.
Identity and network boundaries
Organization hierarchy, service accounts with least privilege, and VPC Service Controls around sensitive datasets.
Analytics that people use
Semantic definitions shared with Looker, dashboards tied to decisions, and a metric dictionary so two teams stop quoting different numbers.
How we work on Google Cloud
Google Cloud projects reward discipline early. We define the organization and folder structure, the dataset layout, and the service account model before the first pipeline lands, because retrofitting boundaries onto a live analytics estate is far more disruptive than setting them up front.
Cost work is concrete rather than theoretical: we read the query history, identify the scans that consume most of the bill, and fix them with partitioning, clustering, and materialisation. Most engagements cut BigQuery spend meaningfully in the first month without changing what people can ask.
Google Cloud in production: where it breaks
The same handful of problems show up on almost every engagement. Naming them early is cheaper than discovering them at 2am.
Full-table scans on every dashboard
SELECT star against unpartitioned tables burns budget silently. Partitioning, clustering, and required filters cut both cost and latency.
Unmanaged service account keys
Downloaded JSON keys spread across laptops and CI. Workload identity and short-lived credentials remove the keys entirely.
Notebooks treated as production
A model that only runs in someone’s session cannot be operated. We move it into scheduled jobs with versioned code and monitoring.
When to use Google Cloud
Analytics-first organisations consolidating warehouses onto BigQuery.
Product teams that need an AI feature in production, not a demo.
Multi-cloud estates needing a governed data landing zone in GCP.