A single, scalable home for all your business data.
Average reduction in cloud data costs post-optimisation
Faster query performance vs legacy on-premise warehouses
Analytics impact on production system performance
Deploy and configure Snowflake, BigQuery, or Redshift tailored to your workload, data volumes, query patterns, and budget constraints.
Design dimensional models, data marts, and semantic layers using dbt that make analytics fast, consistent, and easy for business users to understand and trust.
Cluster keys, materialised views, query optimisation, and auto-suspend policies that minimise compute costs without sacrificing query performance.
Row-level security, dynamic column masking, and role-based access policies so the right people see exactly the right data, and nothing they should not.
Migrated a 12TB on-premise SQL Server warehouse to Snowflake for a professional services firm, reducing infrastructure costs by 55%, cutting query times from minutes to seconds, and eliminating maintenance overhead.
Built a Snowflake warehouse consolidating product usage, billing, support, and CRM data for a SaaS company, enabling their first cross-functional analytics layer and reducing analyst query times by 8×.
Reduced a retail group's BigQuery bill by 48% through partition pruning, clustering, query rewrites, and materialised view implementation, without any reduction in analytics capability.
We bring deep domain knowledge across these sectors
Evaluate current state, data volumes, query patterns, and warehouse requirements.
Design the warehouse architecture, data layers, modelling approach, and security model.
Build the warehouse, migrate existing data, and validate completeness and accuracy.
Tune performance, implement cost controls, and hand over with full documentation.
Snowflake for most mid-market clients, it separates compute and storage elegantly and integrates well with the modern data stack. BigQuery for GCP-first organisations. Redshift for existing AWS-heavy environments. We assess your stack and recommend accordingly.
A basic warehouse with initial data migration can be live in 4–6 weeks. A full implementation with multiple data sources, transformation layers, and BI connectivity typically takes 10–16 weeks.
Yes. We handle migrations from on-premise SQL Server, Oracle, and Teradata to cloud warehouses, including schema translation, data migration, and validation.
We implement auto-suspend policies, query prioritisation, materialised views, and clustering keys. Most clients reduce cloud data costs by 30–50% within 90 days of our optimisation work.
A data warehouse stores structured, processed data optimised for querying and reporting. A data lake stores raw data of any format at lower cost. We often recommend a lakehouse architecture, combining both, for clients with diverse data types and use cases.
We implement dynamic data masking, column-level encryption, row-level security policies, and detailed audit logging. PII and sensitive data can be masked for specific roles while remaining accessible to authorised users.
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