Serverless data warehousing and real-time analytics for enterprises that need to query billions of rows in seconds without managing infrastructure. BigQuery-powered data platforms for analytics teams, data products, and mission-critical reporting.
We embed AI at every stage of the BigQuery development lifecycle from schema design and pipeline orchestration to query cost optimisation and BI layer compliance checks so your analytics platform ships faster and stays production-ready.
Table clustering, partition strategies, nested RECORD columns, and column-level security configurations generated from your data patterns reducing storage costs and query scan volumes from the first table.
Automated query plan reviews surface full-table scans, unpartitioned queries, expensive cross-joins, and reservation under-utilisation before they hit your monthly bill.
dbt models, Dataform workflows, and BigQuery scheduled queries structured from your transformation logic enforcing data quality checks and lineage documentation automatically.
BI Engine reservation sizing, materialised view recommendations, and cached result policies surfaced continuously not discovered after dashboard complaints.
Great Expectations and dbt test suites auto-generated from your schema contracts, with AI-suggested data quality checks for null rates, cardinality, and referential integrity.
Column-level access policies, row-level security, data masking configurations, and VPC Service Controls validated automatically throughout development.
Carefully engineered BigQuery data warehouses built for enterprises that need petabyte-scale query performance, real-time ingestion, and the confidence that their analytics will return accurate, cost-efficient results every time.
We choose the right combination of BigQuery features, transformation frameworks, and surrounding GCP services that fit your data volume, query patterns, cost targets, and team.
Data sources, query patterns, freshness requirements, cost targets, and compliance needs analysed before the first table is created. You leave with a validated data model and ingestion strategy.
Star schema designs, partition strategies, dbt or Dataform project structure, and CI/CD pipeline configured with automated data quality tests before the full build sprint begins.
Friday demo, Friday invoice. Staging BigQuery dataset with real data volumes from sprint 3 so you can validate query costs and dashboard performance on realistic data. Pause anytime.
INFORMATION_SCHEMA query analysis, slot utilisation review, partition pruning validation, and BI Engine performance testing completed before production cutover.
Data source connections, IAM policies, scheduled pipeline runs, and Cloud Monitoring alerts configured before launch. We stay on through your first production reporting cycle.
Stay with us under SLA for ongoing BigQuery optimisation, pipeline maintenance, and cost governance or take it home with full documentation and a 90-day warranty.
Deep teams with industry context not generalists googling compliance acronyms. Each industry below has 30+ shipped projects and a partner who knows the regulator.
Telemedicine, EHR/EMR, claims automation, clinical decision support. HIPAA, HL7/FHIR, GDPR. Active partnerships with 14 hospital networks.
Core banking, neobank, payments, lending, KYC, fraud. PCI DSS, RBI sandbox, Open Banking, ISO 20022. We've shipped to Tier-1 banks in 4 countries.
Headless commerce, marketplace, omnichannel, AR try-on, AI recommendations. Shopify Plus, BigCommerce, custom. 22+ storefronts live with avg +34% AOV.
Last-mile optimisation, TMS, WMS, fleet IoT, route prediction, real-time tracking. Shipped to UPS, Alod and 11 other logistics operators.
OTT platforms, content recommendation, real-time encoding, multi-DRM, distribution at network scale. Sony Pictures, Hello Baby Direct and more.
LMS, adaptive learning, AI tutors, government portals. Shipped UKIERI for the British Council and 6 state-government education portals.
Real names, real companies, real numbers. Video on the left, written notes on the right - choose whichever feels more honest.
Although regulations prevented the site's launch, it met all requirements in terms of form and function. Fullestop's project plan charted a clear course to completion. The team's flexible, diverse talent pool enabled them to manage each stage of the project with consistent levels of skill.
Weekly demos, no surprises, and they push back when we're wrong. That last part is rare. Cut our cloud bill 47% in the first audit.
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BigQuery is Google Cloud's fully managed, serverless data warehouse designed for petabyte-scale analytics. It enables organizations to run SQL queries across massive datasets without managing infrastructure. Fullestop uses BigQuery to build analytics platforms, business intelligence dashboards, machine learning pipelines, and scalable enterprise data warehouses.
BigQuery pricing is based on data storage and query processing. Organizations can choose on-demand pricing or dedicated capacity models depending on workload requirements. Fullestop optimizes schemas through partitioning, clustering, and materialized views to reduce query costs and improve efficiency.
BigQuery processes billions of rows using distributed columnar storage and parallel execution. Query performance depends on schema design, partitioning strategy, clustering, and workload configuration. Fullestop optimizes these factors to deliver fast dashboard and reporting experiences even on very large datasets.
Yes. BigQuery supports real-time data ingestion through integrations with services such as Pub/Sub, Dataflow, and the Storage Write API. Fullestop builds streaming architectures that power live dashboards, operational reporting, event analytics, and near real-time business intelligence solutions.
Fullestop follows a structured migration methodology that includes architecture assessment, schema mapping, historical data migration, pipeline replication, validation testing, and production cutover. We support migrations from platforms including Redshift, Snowflake, SQL Server, Oracle, and other enterprise data warehouse solutions.
Yes. BigQuery supports enterprise-grade security and compliance controls including encryption, audit logging, access management, data residency options, and network security controls. Fullestop configures these capabilities to help organizations align with HIPAA, GDPR, and other industry-specific compliance requirements.
Yes. BigQuery integrates natively with leading business intelligence platforms including Looker Studio, Looker, Tableau, and Power BI. Fullestop designs optimized data models and semantic layers that improve reporting performance while minimizing query costs.
BigQuery ML allows organizations to create, train, and deploy machine learning models directly within the data warehouse using SQL. For advanced use cases, Fullestop integrates BigQuery with Google Cloud AI services to build scalable predictive analytics, forecasting, recommendation systems, and enterprise AI workflows.
A focused analytics platform with ingestion pipelines, data models, and initial dashboards typically takes six to twelve weeks. Larger enterprise implementations involving multiple data sources, governance frameworks, and machine learning capabilities require additional planning and phased delivery.
Fullestop delivers BigQuery-based analytics and data platforms for healthcare, fintech and BFSI, retail and eCommerce, logistics and supply chain, media and entertainment, education technology, and public sector organizations. Industry requirements guide the design of data models, compliance controls, KPIs, and reporting frameworks.
