Serverless petabyte-scale analytics on GCP - warehouse design, streaming ingestion, SQL optimisation, BI Engine, and full pipeline delivery on BigQuery.
Google BigQuery eliminates infrastructure management and lets analysts run SQL over petabytes of data in seconds. But the difference between a fast, cost-efficient BigQuery environment and an expensive, fragile one comes down entirely to how it's designed.
At DynamicUnit, we design and deliver BigQuery environments that are partitioned, clustered, and modelled correctly from day one. We build the ingestion pipelines, transformation layers, and BI Engine configurations that ensure your analysts get fast answers - and your finance team doesn't get a surprise billing spike. Our Looker Studio reporting practice pairs naturally with BigQuery for end-to-end analytics delivery.
Whether you need a greenfield warehouse, a migration from Redshift or Snowflake, or optimisation of an existing environment, we handle the full pipeline - from data warehousing design through to production dashboards. Need to feed BigQuery from legacy systems? Our data migration and Python pipeline teams have done this across dozens of engagements.
Customer behaviour analytics, product performance reporting, inventory forecasting, and marketing attribution across millions of daily transactions.
Transaction monitoring, risk scoring, regulatory reporting, and fraud detection pipelines processing petabytes of financial event data.
Fleet tracking analytics, route optimisation data, warehouse throughput dashboards, and demand planning models built on streaming IoT data.
Clinical trial data aggregation, patient outcome analytics, operational dashboards for hospital networks, and HIPAA-compliant data governance.
From initial dataset design to production-grade pipelines and BI tooling - here's what our BigQuery practice delivers.
Schema design with star/snowflake models, correct partitioning strategies, and clustering keys that keep query costs low at scale.
Real-time data ingestion via Pub/Sub, Dataflow, and the BigQuery Storage Write API - with exactly-once delivery guarantees.
Build reliable batch and incremental pipelines using Dataflow, Cloud Composer (Airflow), and dbt for transformation at any volume.
Rewrite expensive queries, fix slot contention, implement materialised views, and resolve performance bottlenecks in existing environments.
Configure BigQuery BI Engine reservations for sub-second Looker Studio dashboards, and build semantic models in Looker for self-serve analytics.
Build and deploy machine learning models directly in BigQuery SQL - from forecasting and anomaly detection to recommendation engines.
Implement column-level security, row-level access policies, VPC Service Controls, and data catalogue tagging for enterprise governance.
Analyse slot usage, identify expensive queries, configure reservations vs on-demand billing, and set up spend alerts to control GCP costs.
A lot of teams can stand up a BigQuery dataset. Fewer can design a warehouse that performs at petabyte scale without runaway costs, schema drift, or brittle pipelines. Here's what we do differently.
We model your data before writing a single pipeline - ensuring the physical design supports both current query patterns and future growth.
We design partitioning, clustering, and slot configurations to minimise query costs. Clients typically see 40–70% cost reduction after optimisation.
We use the right GCP services - Pub/Sub, Dataflow, Cloud Composer, Data Catalog - rather than forcing third-party tools where native services perform better.
Every pipeline includes monitoring, alerting, retry logic, and data quality checks. We don't ship pipelines that only work when everything goes right.
Column-level security, data lineage, audit logging, and IAM role design are built in from the start - not retrofitted when compliance asks for them.
We document everything and run hands-on sessions so your data team can extend and maintain the platform independently after handover.
We assess your existing data sources, query patterns, team skill levels, and reporting requirements. You get a clear architecture proposal with cost projections.
We design the warehouse schema, build ingestion pipelines, configure partitioning and clustering, and set up the data warehousing foundation with proper governance.
We connect Looker Studio or your preferred BI tool, configure BI Engine for sub-second dashboards, and validate that reports answer the questions your team is actually asking.
Knowledge transfer sessions for your data team, cost monitoring dashboards, and documentation. We also offer ongoing managed support for pipeline monitoring and query optimisation.
Tell us your data volumes, current stack, and analytical goals - we'll show you what a well-engineered BigQuery environment looks like for your use case.
