BigQuery for analytics
A place to ask questions of your data that would bring a transactional database to its knees.
Analytics warehouse
Chosen per project
Maintainable handover
Operational databases are built to record transactions quickly, not to scan three years of them. Once reporting starts competing with day-to-day work — and the month-end report locks the sales screens — the answer is to separate the two.
BigQuery is designed for the second job: large scans, aggregation and joins across sources, charged by the data a query reads. Structured well, that makes analysis cheap; structured badly, it makes a single careless query expensive, which is why partitioning matters.
What we build with BigQuery
Reporting that has outgrown the transactional database
Combining sales, inventory and web analytics in one place
Dashboards for management that must not affect live operations
Retaining history that would bloat the operational system
Where it fits — and where it does not
Good fit when
Analytical queries over large volumes
Data from several systems that has to be joined
Reporting that should be isolated from live load
Consider something else when
Row-by-row transactional updates — that is not what it is for
Small datasets a well-indexed database handles comfortably
From operational system to dashboard
Each stage exists so a change at one end does not break the other.
Extract
Scheduled export from the source system.
Land raw
Stored unchanged, so it can be reprocessed.
Model
Cleaned, joined, business rules applied once.
Report
Dashboards read the modelled layer only.
How we work with BigQuery
Partition and cluster
Tables partitioned by date and clustered on the columns actually filtered on, because that is what governs both speed and cost.
Load, do not couple
Scheduled loads or streaming inserts from the operational system, never dashboards querying production directly.
Modelled layers
Raw, cleaned and reporting layers kept separate so a report change never rewrites ingestion.
Cost guardrails
Query limits and cost alerts set before analysts are given access.
Our typical BigQuery setup
| Concern | What we use |
|---|---|
| Loading | Scheduled batch loads, or streaming inserts where freshness matters |
| Modelling | SQL transformations in versioned, reviewable files |
| Performance | Date partitioning plus clustering on filter columns |
| Access | Dataset-level IAM; analysts never touch production systems |
| Cost | Per-query byte limits and budget alerts |
Frequently asked questions
Not until reporting starts hurting the operational system or you need to join data across systems. Before that, a read replica and good indexes are cheaper and simpler.
Principally on the bytes a query scans, plus storage. Partitioning and selecting only the columns needed are what keep that number small, and both are design decisions made up front.
Yes — Looker Studio, Power BI and most BI tools connect directly, reading from the modelled layer rather than production.
Topics people search for
Services built with BigQuery
AI/ML Development
Machine learning applied where it measurably beats a simpler solution — and honest advice when it does not.
Enterprise Software Development
Systems for organisations where several departments, sites and approval chains all have to work from the same data.
Digital Transformation
Moving a business off paper and spreadsheets without stopping the business to do it.
Cloud Solutions
Hosting designed around what your application actually needs, with the monitoring and backups that make it safe to rely on.
Tell us what you are trying to build
Describe the problem in plain language and we will tell you what it would take to solve it — the approach, the moving parts and the sensible order to build them in. No obligation either way.