BigQuery & Data Analytics Services
AI is only as good as the data underneath it. We design and build the BigQuery foundation — encompassing the warehouse, the pipelines, business intelligence, and governance — so that data which is scattered around can be turned into a single source of truth that both your models and your teams can rely on.
When BigQuery is the right answer
The customer relationship management functions are contained in one system, the operations in another and finance is managed using spreadsheets, which means that any question that involves more than one team takes a week of data exporting.
The dashboards are updated once a month and the analysts spend their time copying the data instead of analyzing it.
All AI projects come to a halt at the same obstacle, namely that the training data is either inconsistent, undocumented, or unreachable.
What we build on BigQuery
Warehouse & lakehouse design
BigQuery architecture modeled on the way your business actually asks questions, including schemas, partitioning, and access design, ensuring that it remains fast even as the volume of data increases.
Data pipelines
Using Dataflow, Pub/Sub, and Cloud Composer for both batch and streaming ingestion means that the process is reliable, closely monitored, and completely documented, right from the source systems up to the warehouse.
BI & dashboards
Looker and Looker Studio produce reports that answer the questions which leaders actually ask, offering self-service where this is appropriate and ensuring governance is in place when it is needed.
BigQuery ML & AI integration
Forecasting and classification models that run where your data already lives, with a clean handoff to Vertex AI for advanced machine learning — no shuffling of data between platforms.
Governance & security
Lineage, cataloging, row- and column-level security, and IAM design — so that only the right people see the right data and auditors can see the paper trail.
Cost optimization
Using partitioning, clustering, the slot strategy, and following query hygiene helps to make sure that BigQuery billing stays predictable, with many cases seeing cost reductions during the redesign process.
Where this earns its keep.
Every engagement runs spec-first, agent-accelerated, human-owned.
A written specification that has been approved by the client prior to any coding taking place — covering the architecture, the edge cases, and the acceptance criteria.
The engineers arrange for the AI agents to carry out the specification in parallel, taking weeks rather than quarters.
The evaluations, tests, and security checks are carried out together with the build, and humans examine every decision that is important.
The system has been deployed to your GCP or AWS, complete with monitoring, production evaluations, and retraining loops.
From the portfolio.
Turning raw user feedback into actionable insight
A production-grade pipeline on Google Cloud which automatically summarizes rating-based comments and identifies their sentiment, thereby converting large amounts of unprocessed user feedback into insights that teams can act on.
Tools we reach for.
Chosen for each project based on evaluations and fit — never out of habit.
Questions, answered.
Can you migrate us from Redshift, Snowflake, or an on-prem warehouse?
Do we need real-time data, or is daily enough?
How does this connect to our AI plans?
How do you keep BigQuery costs under control?
Who maintains the pipelines after you leave?
Planning something on Google Cloud? Let's pressure-test it — free.
A 30-minute call with a certified engineer, not a salesperson. You'll get an honest feasibility read, a rough architecture, and a clear next step.
Book a free AI consultation