Most Deployments Use 10% of What It Can Do.BigQuery as a destination for scheduled SQL exports is not a data platform — it is a slow reporting database with a different billing model. BigQuery with Dataflow streaming pipelines, partitioned and clustered tables, BI Engine for sub-second dashboard response, and Data Catalog for governed discovery is a different category of capability. Symhas builds the latter.Symhas designs and deploys the full GCP data platform — BigQuery data warehouse with optimal schema design, Dataflow batch and streaming pipelines, Pub/Sub event ingestion, Cloud Storage as the raw data lake, and dbt for transformation — production-ready, cost-optimised, and governed from day one.
Production-Grade on GCP.
Every capability designed, deployed, and documented by Symhas GCP-certified data and AI architects. Fixed price. Production-ready.
Fixed Price. Fixed Timeline.
Four phases with go/no-go gates. Scope and price agreed before week one.
Source system inventory and data volume profiling. BigQuery schema design. Dataflow pipeline architecture. Pub/Sub topic design. dbt project structure and medallion layer design. Cloud Storage bucket and lifecycle design. Architecture approved at go/no-go gate.
BigQuery datasets created with partitioning and clustering. Cloud Storage buckets with lifecycle policies deployed. Dataflow pipelines built and unit tested. Pub/Sub topics and subscriptions configured. dbt project scaffolded with source definitions.
dbt staging, intermediate, and mart models built. dbt tests for schema validation and data quality implemented. BI Engine reservations configured. Data Catalog tags applied to all BigQuery tables. End-to-end pipeline tested with production-volume data.
Production pipelines live. Cost monitoring alerts configured. Data engineering team certified on dbt workflow and Dataflow operations. Runbooks for pipeline failure and backfill documented. Symhas moves to advisory.
33% Forecast Accuracy Gain. 21% Holding Cost Reduction. 12 Weeks.
A national retailer with 500+ locations needed unified demand forecasting across all sites — previously running 12 separate reporting databases with no single source of truth for inventory and sales data. Forecast accuracy was measured at under 60% on slow-moving SKUs.
Symhas built a GCP data platform — BigQuery as the unified warehouse ingesting POS, inventory, and supplier data via Dataflow, dbt transformation producing a medallion architecture, and Vertex AI forecasting models trained on the BigQuery feature dataset. Forecast accuracy rose to 93% on slow-moving SKUs.
GCP Data Platform — Retail Production Deployment
“We had 12 reporting databases that disagreed with each other on every metric. BigQuery gave us one number. dbt gave us the lineage to prove where that number came from. That is a different conversation with the business.”
— Chief Data Officer, National Retailer
We Configure for This Capability.
Serverless petabyte-scale warehouse — schema design, partitioning, clustering, BI Engine, and column-level security.
Batch and streaming pipelines — Apache Beam, exactly-once semantics, auto-scaling, and Flex Templates.
Event streaming — topic and subscription design, BigQuery subscriptions, dead-letter, and message ordering.
Data lake — bucket design, lifecycle policies, raw/processed/archive tiers, and transfer automation.
Transformation layer — medallion architecture, source definitions, mart models, and data quality tests.
Data governance — table and column tagging, business glossary, PII classification, and data lineage.
ML models trained on the BigQuery data platform this page describes.
Oracle ERP to GCP IntegrationGoldenGate CDC pipelines bringing Oracle Fusion data into this BigQuery platform.
Looker Analytics & BILooker semantic layer and dashboards built on top of this BigQuery data warehouse.
GCP Security & GovernanceVPC Service Controls and data governance protecting the BigQuery data platform.
We Will Design the BigQuery Platform That Connects Them.A 30-minute GCP data assessment with a Symhas BigQuery architect. We will review your source systems, data volumes, and reporting requirements — and design the data platform architecture before the engagement price is agreed.No commitment. No pitch deck. An honest conversation about your data and AI ambitions.