Data Warehouse Experts

BigQuery Analytics —
From Raw Data to Business Decisions

Google BigQuery is the serverless, petabyte-scale data warehouse that eliminates infrastructure management for analytics. We design BigQuery schemas, build streaming ingestion pipelines, implement BigQuery ML for in-warehouse machine learning, and connect Looker Studio for BI — giving your team real-time insight into business data without managing servers.

Build Your Data Warehouse → GCP Overview

Petabyte

Scale — no cluster sizing

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. For clients coming from self-managed Postgres or MySQL for analytics, the shift is transformative: queries that took hours on OLTP databases run in seconds on BigQuery, and there are no indexes to tune, no vacuums to run, no disk capacity to manage.

We design multi-layered BigQuery implementations: raw data landing zones, transformation layers (using dbt or BQML procedures), curated datasets optimized for BI tools, and row-level security so different users see only the data they're authorized to access.

✓ Partitioned and clustered tables for cost-optimized query performance

✓ Pub/Sub → BigQuery streaming for sub-minute data freshness

✓ dbt (data build tool) for modular, tested transformation logic

✓ BigQuery ML: regression, classification, and LLM models in SQL

✓ Authorized views and row-level security for multi-tenant data

✓ Looker Studio and Looker for self-service business intelligence

Financial Analytics Warehouse

Consolidate transaction data, ledger events, and CRM data into a governed BigQuery warehouse. Build dashboards for portfolio performance, risk exposure, and regulatory reporting — updated in near real-time.

Machine Learning Pipelines

Use BigQuery ML to train fraud detection models, customer churn predictors, and LTV estimators directly on your data warehouse — no data extraction, no separate ML infrastructure.

Operational Data Store

Replace ad-hoc reporting queries on your OLTP database with BigQuery materialized views and scheduled queries. Remove analytics load from production systems and give analysts a safe sandbox.

Multi-Source Data Integration

Combine data from Stripe, HubSpot, your core banking system, and custom APIs into a unified BigQuery model. We build scheduled extractions, schema mappings, and deduplication logic.

BigQuery FAQ

How does BigQuery pricing work? Can costs get out of control?+

BigQuery pricing has two models: on-demand (you pay per TB of data scanned by queries) and committed slots (reserved compute capacity). For most clients starting out, on-demand is cost-effective and predictable when queries are properly designed (using partitioned tables, column selection, and materialized views to minimize scan volume). We implement cost controls — query cost estimators, dataset quotas, and Slack alerts on high-cost queries — so there are no billing surprises.

We currently use PostgreSQL for all analytics. Should we migrate to BigQuery?+

If your analytics queries are slowing down production Postgres, scanning large tables, or requiring complex aggregations across millions of rows, BigQuery is the right answer. The migration usually involves setting up a parallel BigQuery dataset, replicating historical data, and migrating BI queries one by one. You keep Postgres for OLTP (transactions, writes) and use BigQuery exclusively for analytics. We manage end-to-end.

What's BigQuery ML and when is it useful?+

BigQuery ML lets you train machine learning models using SQL — no Python, no Jupyter notebooks, no data export needed. Models include linear/logistic regression, random forests, boosted trees, and even LLM integration via Vertex AI. It's most useful when your data science team needs fast iteration on tabular data (fraud scoring, churn prediction) and doesn't want to manage model infrastructure. For production-grade models requiring custom architectures, we use Vertex AI training alongside BigQuery as the data source.

How do you handle data governance and access control?+

BigQuery has excellent IAM integration. 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 accessing raw tables. All access changes are audited via 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 BigQuery setup (landing zone, one source integration, one BI dashboard) takes 2–3 weeks. A full data platform with multiple sources, dbt transformations, ML models, and a governed BI layer is typically a 2–3 month engagement. We deliver in phases — you get actionable insights in week one, not month three.

BigQuery Analytics — From Raw Data to Business Decisions