Better Reporting with BigQuery + Looker Studio

Analytics becomes actionable when your warehouse and reporting layer work together. BigQuery provides elastic storage and compute; Looker Studio turns trusted data into clear, shareable insights for every team.

Begin with a semantic model that mirrors business logic—metrics, dimensions, and joins—so every dashboard is consistent and credible.

If the model is wrong, the dashboard will mislead. Invest in the model.

Architecture at a Glance

Land raw data in BigQuery, transform into clean business tables, then visualise in Looker Studio using parameterised and optimised queries with sensible caching.

Data Modelling in BigQuery: Use layered datasets (raw → staging → mart). Standardise keys, timestamps, and units. Create wide fact tables with clear dimensions to minimise joins in dashboards.

Performance Tips: Partition large tables by date, cluster by high‑cardinality columns, and pre‑aggregate frequent metrics. In Looker Studio, bind filters to parameters, enable cache where appropriate, and limit uncontrolled drill‑downs.

Governance & Access: Apply IAM at dataset/table levels, use authorised views for sensitive fields, and define metric ownership. Document lineage and dashboard purpose so adoption scales without confusion.

Visualisation Patterns: Highlight what matters with small multiples, comparison bars, and trend lines for KPIs. Use consistent colour scales, annotate targets and thresholds, and offer guided filters (date ranges, segments) so stakeholders answer common questions quickly and confidently.

• Model before you visualise
• Partition + cluster for speed
• Pre‑aggregate heavy queries
• Use parameters for controlled interactivity
• Document KPIs and definitions

Conclusions

BigQuery + Looker Studio turns raw data into decisions when the warehouse, model, and dashboard are aligned. With solid modelling, performance tuning, and governance, teams get fast, consistent insights they trust.