Google BigQuery

Google BigQuery is my go-to platform when data from shops, ERP systems, and SaaS tools must come together fast. The fully managed warehouse ingests huge datasets without server maintenance and powers real-time dashboards, automation, and AI workloads.

Why BigQuery?

• Serverless scale – No infrastructure to manage, automatic scaling for spikes and seasonal peaks.
• SQL-first – Analysts and engineers keep using standard SQL with window functions, JSON columns, and ML extensions.
• Open ecosystem – Native connectors for Google Ads, GA4, Shopware feeds, plus external tables via Cloud Storage or S3.
• Pay-as-you-go – Storage & compute are billed separately, perfect for bursty reporting workloads.
• Security & compliance – IAM, CMEK, and fine-grained column policies satisfy EU compliance requirements.

Typical use cases

• Commerce analytics that blends Shopware, VARIO ERP, and payment data for gross margin tracking.
• Automation backends where n8n or Laravel jobs stream data into BigQuery and trigger downstream actions.
• Customer 360° views that join CRM, support, and marketing datasets for segmentation.
• Data products such as APIs or embedded dashboards built on top of curated BigQuery views.

Services with BigQuery

• Data modeling & ingestion – Design schemas, partitioning, clustering, and ELT pipelines (Airbyte, dbt, custom).
• Streaming & batch connectors – Build Cloud Functions, Cloud Run, or Laravel workers to push events in near real time.
• BI delivery – Expose Looker Studio, Metabase, or custom Nuxt dashboards with row-level security.
• Cost optimisation – Slot reservations, materialized views, and monitoring to keep spend predictable.
• Enablement – Workshops, runbooks, and CI/CD setup for versioned SQL workflows.

Integrations & tooling

• Shopware / WordPress / Laravel exports via APIs or direct database sync.
• VARIO ERP data replication (SQL dumps or CDC) for finance and logistics metrics.
• Google Sheets & Docs round-tripping with Connected Sheets for business-friendly reporting.
• n8n & Make.com automations that enrich or distribute warehouse insights.
• AI workloads by feeding curated datasets into Vertex AI, Claude, or custom LLM services.

Operations & governance

• Managed deployments via Terraform or Pulumi with environment parity.
• Monitoring hooks for Dataform/dbt tests, Stackdriver alerts, and incident playbooks.
• Data quality SLAs with automated validation and reconciliation scripts.
• Documentation in Confluence/Notion plus shared semantic layers for self-service teams.

Conclusion

BigQuery lets organisations move from siloed exports to reliable analytics products quickly. I combine architecture, implementation, and enablement so your team gains insights without adding operational overhead.