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Technology / BigQuery
Data Warehouse Experts

From Raw Data to Business Decisions with BigQuery Analytics

Google BigQuery is a serverless data warehouse that scales to petabytes without anyone managing a cluster. We design the schemas, build the streaming ingestion pipelines, implement BigQuery ML to train models directly on the data, and connect Looker Studio for dashboards, so your team works with current data without running a single server.

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Petabyte
Scale without sizing clusters
Serverless
Pay per query, not per hour
Streaming
Millisecond data freshness
ML-Native
Train models inside SQL

Analytics at Any Scale

BigQuery separates compute from storage: you pay for the storage you use and the queries you run. Clients coming from self-managed Postgres or MySQL usually notice the difference right away. Queries that took hours on an OLTP database run in seconds here, and there are no indexes to tune, no vacuums to schedule, no disk capacity to watch.

We design these implementations in layers: a landing zone for raw data, a transformation layer built with dbt or BQML procedures, curated datasets ready for BI tools, and row-level security so each user sees only what they're supposed to.

✓ Partitioned and clustered tables for cost-optimized query performance
✓ Pub/Sub to BigQuery streaming for sub-minute data freshness
✓ dbt (data build tool) for modular, tested transformation logic
✓ BigQuery ML: regression, classification, and LLM models directly in SQL
✓ Authorized views and row-level security for multi-tenant data
✓ Looker Studio and Looker so the business can query its own data

Financial Analytics Warehouse

We consolidate transaction data, ledger events, and CRM data into a governed BigQuery warehouse, with dashboards for portfolio performance, risk exposure, and regulatory reporting that update in near real-time.

Machine Learning Pipelines

We use BigQuery ML to train fraud detection, churn prediction, and LTV models directly on your warehouse, with no data extraction and no separate ML infrastructure to maintain.

Operational Data Store

We replace ad-hoc reporting queries against your OLTP database with BigQuery materialized views and scheduled queries, which takes the analytics load off production systems and gives analysts a safe place to work.

Multi-Source Data Integration

We combine data from Stripe, HubSpot, your core banking system, and custom APIs into one BigQuery model, building the scheduled extractions, schema mappings, and deduplication logic that keep it consistent.

BigQuery FAQ

How does BigQuery pricing work, and can it get out of hand?+
BigQuery pricing has two models: on-demand, where you pay per TB of data scanned per query, and committed slots, which is reserved compute capacity. For most clients starting out, on-demand is cost-effective and predictable as long as queries are designed well, using partitioned tables, selecting only the columns you need, and relying on materialized views to cut down scan volume. We also set up cost controls, like query cost estimators, dataset quotas, and Slack alerts on expensive queries, so nothing shows up as a surprise on the bill.
We currently run all our analytics on PostgreSQL. Should we move to BigQuery?+
If your analytics queries are slowing down production Postgres, scanning large tables, or running complex aggregations across millions of rows, yes, BigQuery is the right move. The migration usually means standing up a parallel BigQuery dataset, backfilling historical data, and moving your BI queries over one at a time. Postgres stays for OLTP work, transactions and writes, while BigQuery takes over analytics entirely. We manage the whole process.
What's BigQuery ML, and when is it actually useful?+
BigQuery ML lets you train machine learning models using SQL, no Python, no Jupyter notebooks, no exporting data elsewhere. It covers linear and logistic regression, random forests, boosted trees, and even LLM integration through Vertex AI. It's most useful when your data team needs to iterate quickly on tabular data, like fraud scoring or churn prediction, without standing up separate model infrastructure. For production models that need custom architectures, we train in Vertex AI instead and use BigQuery as the data source.
How do you handle data governance and access control?+
BigQuery integrates well with IAM. We implement column-level security for PII fields, row-level security for multi-tenant datasets, and authorized views so analysts can query curated datasets without touching raw tables. Every access change is logged through Cloud Audit Logs. For regulated clients, we configure VPC Service Controls so BigQuery data never leaves your private network boundary.
How long does it take to set up a BigQuery data warehouse?+
A basic setup, a landing zone, one source integration, one BI dashboard, takes about 2 to 3 weeks. A full data platform with multiple sources, dbt transformations, ML models, and a governed BI layer is usually a 2 to 3 month engagement. We deliver in phases, so you get usable insight in week one instead of waiting until month three.

Put Your Data Warehouse to Work

Stop running reports against your production database. We'll design a BigQuery architecture that's fast and reliable, with a cost structure that scales the way your usage does.

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