BigQuery
warehouse
BigQuery Consulting & Implementation
Most teams on BigQuery are overpaying for scans they never needed to run.
We design the partitioning, clustering, and cost controls that make BigQuery the cheapest warehouse your team has operated — and the fastest. Senior engineers, production-grade from the first commit.
BigQuery · live signal
What we've shipped with BigQuery
0+
projects hardened
0%
avg scan cost cut
<0
second queries
Point of view
Why most BigQuery bills are wrong
BigQuery's serverless model is its best feature and its biggest trap. You don't manage capacity, so nothing stops a single un-partitioned query from scanning a terabyte to return ten rows. The bill arrives at the end of the month and nobody can point to which query did it.
We've seen the same pattern across e-commerce, fintech, and SaaS teams: a warehouse that works fine at 10GB and quietly becomes a cost problem at 1TB. The data didn't get smarter. The query patterns didn't change. The scans just got expensive.
The fix is rarely "use a different warehouse." It's partitioning and clustering aligned to how your team actually queries, on-demand vs. slot-based pricing chosen deliberately instead of by default, and cost controls that alert before the bill, not after. Done right, the same workload runs on a fraction of the scan volume.
We implement this the way it should have been built the first time — datasets and projects mapped to your billing model, partitioning that cuts scan cost on your real query patterns, and IAM that a security review won't flag. Production-grade from the first commit: tested, documented, handed off.
Representative result — same workload, the data didn't change, the scans got expensive.
Before partitioning
0.00 TB
After partitioning
0.00 TB
When BigQuery makes sense
Pick BigQuery when…
- You're on Google Cloud (or want to be)
- You want fully serverless analytics
- You need to query massive data without capacity planning
- You want native geospatial / ML / streaming
What we implement
Beyond the tutorial.
01
Project structure
Datasets, projects, and folders mapped to your org and billing model, so cost is attributable per team from day one. Dev/staging/prod separation that doesn't leak.
02
Modelling
Partitioning and clustering designed around your actual query patterns, not generic defaults. We measure scan bytes before and after — fewer bytes scanned per query is the only number that moves the bill.
03
Cost governance
Slots vs. on-demand chosen deliberately, per-team budgets, and query cost alerts that fire before a runaway query becomes a runaway invoice.
04
Security
IAM, row-level access, column masking, and Cloud DLP integration that passes a real security review, not just a checkbox.
In production
What this looks like in production
We don't theorise about scan cost and query performance — we ship it. Two engagements where the discipline above moved the number that mattered.
Finsights — finance SaaS
0+
tenants on multi-tenant embedded analytics, built on BigQuery
Sub-second query performance · under 1s at p95
Netthandelsgruppen — e-commerce
0%
reduction in reporting time after unifying Shopify, QuickBooks & marketing data
Ran on Databricks — identical discipline: model for the query pattern, control the cost
FAQ
Questions we get a lot.
What does a BigQuery consulting engagement actually include?
A diagnosis first — we look at your current scan costs, query patterns, and project structure before prescribing anything. Then implementation: partitioning and clustering, cost governance, IAM and security, and the dbt modelling layer if you need it. Every engagement ends with documentation and a team that can operate it without us.
How is this different from hiring a BigQuery developer or a big agency?
You work with senior engineers directly — the people doing the work, not a rotating bench of juniors. We take fewer clients so each one gets real attention. If your problem is a $2K fix, we'll tell you that instead of selling a migration.
Can you reduce our existing BigQuery bill?
Usually, yes. The most common cause of a high BigQuery bill is un-partitioned tables and queries that scan far more than they need. We measure scan bytes per query, fix the partitioning and clustering, and the bill follows. The savings come from scanning less data, not from a different price.
Do you work with teams outside the US?
Yes. We're based in Mumbai and deliver globally — clients across the US, Europe, and the Middle East. Async-friendly, time-zone aware.