Data models & queries
Solid schemas, indexes and queries for stable performance.
Google BigQuery
Serverless data warehouse for analytics, automation, and commerce reporting.
Services
Reporting & analytics
Dashboards, exports and pipelines — from SQL to BigQuery.
Performance tuning
Find bottlenecks, add caching and optimise for real workloads.
I use Google BigQuery when data from commerce, ERP and SaaS needs to be combined quickly without running a data warehouse as an operational burden. As a serverless service, BigQuery scales automatically and provides a strong foundation for reporting, automation and data‑driven product decisions.
From raw data to reliable KPIs
Commerce setups often spread data across multiple systems: Shopware, payments, ERP, marketing and support. BigQuery lets you consolidate sources through ETL/ELT pipelines so metrics like margin, stock turnover or campaign performance are calculated consistently. A clean model, partitioning/clustering and data quality checks are essential so dashboards stay trustworthy.
Architecture, cost control and governance
BigQuery is SQL‑first, but costs are influenced by queries and storage. I design workloads to remain fast and predictable through smart aggregation, materialised views, monitoring and clear access rules. IAM, encryption and column‑level controls help meet compliance requirements.
Reporting, automation and AI workloads
Dashboards (Looker Studio, Metabase) and exports can sit on top of BigQuery, but so can automation. n8n workflows or Laravel jobs can stream events into BigQuery and trigger follow‑up actions. Connected Sheets is a strong option for teams that prefer spreadsheets. If ML/AI becomes relevant later, curated datasets provide a great base for tools like Vertex AI.
BigQuery turns siloed data into an analytics product you can trust. With clean modelling and governance, you get dashboards and automation without additional operations overhead.