No Servers. No Cluster. Just SQL.
BigQuery is Google Cloud's fully serverless data warehouse. There is no cluster to size, patch or keep running — you load data, write SQL, and Google throws however much compute the query needs at it.
That makes it unusually good for spiky, unpredictable analytics: a query over a petabyte costs the same whether you run it once a month or never. The flip side is that careless SQL scans — and bills — grow fast, which is exactly where good engineering pays for itself.
Zero Infrastructure
Nothing to provision, patch or scale. There is no cluster, so there's no cluster to get wrong.
Petabytes in Seconds
Massively parallel execution means dataset size stops being the thing that limits your questions.
ML Without Moving Data
BigQuery ML trains and serves models in SQL, right where the data already lives.
Built for Scale & Speed
The capabilities that make BigQuery the default warehouse on Google Cloud.
Fully Serverless
No nodes, no scaling policy, no maintenance window. Compute appears when a query needs it.
BigQuery ML
Train regression, classification and forecasting models with CREATE MODEL — in plain SQL.
Streaming Ingestion
Stream events in and query them within seconds — dashboards reflect now, not last night.
Federated Queries
Query Cloud Storage, Sheets and Cloud SQL in place, without loading them first.
Partitioning & Clustering
Prune scans so queries read a fraction of the table — the single biggest cost lever.
Native Looker & Sheets
Business users explore live data in tools they already know, with no export step.
End-to-End BigQuery Services
From ingestion to a query bill that stays predictable.
Warehouse Design
Datasets, partitioning and clustering modelled around how your teams actually query.
Migration to GCP
Move from on-prem, Redshift or Snowflake with a phased plan and validated parity.
Data Pipelines
Dataflow, Pub/Sub, Datastream and dbt - AI drafts the pipeline boilerplate and data-quality tests, a developer reviews them before they run.
Cost Control
Slot reservations, scan pruning and query rewrites — the difference between a sane bill and a scary one.
Governance & Security
IAM, column-level security, masking and audit logs for regulated data.
BI & ML Enablement
Looker, Power BI and BigQuery ML models wired to a layer the business trusts.
Partitioning and Clustering That Pay for Themselves
In BigQuery, the bytes a query reads drive both its speed and, on on-demand pricing, its cost. Good table design means most queries touch only the slice of data they actually need, instead of the whole history of the business.
We also lean on BigQuery's strengths rather than copying a legacy star schema line for line: nested and repeated fields (STRUCT and ARRAY) often replace expensive joins, and wide, denormalised tables are frequently both faster and cheaper here.
Partition on time
Partition large tables by an event date or ingestion time, and require a partition filter so nobody accidentally scans years of data. Partition expiration keeps raw staging tables from growing forever.
Cluster on filters
Cluster by the columns you filter and join on most, such as customer, region or product, so BigQuery prunes storage blocks inside each partition.
Pre-compute the hot paths
Materialized views and scheduled aggregate tables serve dashboards that run the same query hundreds of times a day, at a fraction of the scan cost.
On-Demand or Slot Capacity?
BigQuery offers two ways to pay for compute. Picking the right one, and fencing it in, is the biggest lever on your bill.
On-demand
You pay per byte scanned. Simple and ideal for small or unpredictable workloads, but one unfiltered SELECT * over a large table shows up directly on the invoice.
Capacity (slots)
BigQuery editions let you reserve slots with autoscaling and assign them to projects or teams. Better for steady, heavy usage where a predictable compute bill matters more than per-query cost.
Guardrails either way
Custom query quotas, maximum bytes billed per query, budget alerts and regular reviews of the INFORMATION_SCHEMA jobs views to catch the queries that cost the most.
From Warehouse to Decisions
BI Engine adds an in-memory acceleration layer for dashboards in Looker, Looker Studio and other connected tools, so interactive reports respond quickly without extra scans. We pair it with a governed semantic layer, so every team uses the same definition of revenue, churn or active user.
With BigQuery ML, analysts can build forecasting, classification and anomaly-detection models in SQL, and call Vertex AI models when something more advanced is needed, all without exporting data to another system.
- Assess - inventory tables, jobs and reports in Redshift, Snowflake, Teradata or on-prem SQL Server, and rank them by business value.
- Translate and move - convert SQL dialects (BigQuery's migration service helps with batch translation), then load history through Cloud Storage or the Data Transfer Service.
- Validate in parallel - run old and new side by side and reconcile row counts and key metrics before cutover.
- Clear terms - transparent, milestone-based pricing, an NDA before kickoff, and you own all code and IP.
Pixel-perfect software, delivered at AI speed
PixoBots stands for Pixel & Bots. Our Bots are dedicated developers who work with AI tools: AI takes the repetitive work, a developer reviews every line, and the result is pixel-perfect.
Pixel
Polished UI and clean, tested code - detail is part of the job, not an afterthought.
Bots
Dedicated developers who join your team and use AI for boilerplate, tests and documentation.
Savings
AI-assisted delivery can save more than 50% of development cost compared with traditional development.
Ready to Query at Any Scale?
Let our engineers build a BigQuery warehouse that answers fast — without the runaway query bill.