Data Warehouse Design
Snowflake schema design — dimensional modelling, raw/staging/mart layer architecture and query performance optimisation.
We design and build Snowflake data platforms — data modelling, ELT pipelines with dbt, data sharing and performance-optimised SQL analytics.
Snowflake schema design — dimensional modelling, raw/staging/mart layer architecture and query performance optimisation.
Data ingestion from databases, APIs and SaaS tools into Snowflake — Fivetran, Airbyte or custom connectors with dbt transformations.
dbt Core and dbt Cloud for modular, tested, documented SQL transformations — version-controlled data pipelines.
Snowflake's secure data sharing for sharing datasets with partners, clients or subsidiaries — without data movement.
Python in Snowflake via Snowpark — run pandas, scikit-learn and custom ML code directly on your Snowflake data.
Role-based access control, column-level security, Dynamic Data Masking and row-level policies for enterprise governance.
Snowflake for multi-cloud flexibility, rich data sharing features and SQL-heavy enterprise teams. BigQuery for GCP-native workloads and when Vertex AI integration matters.
Yes — dbt is our standard transformation layer on top of Snowflake. It brings software engineering practices (version control, testing, docs) to SQL.
You pay for storage (per TB) and compute (credits per second of warehouse usage). We design credit-efficient queries and right-sized warehouses to control costs.
Typically yes — Snowflake replaces Redshift, Teradata, SQL Server and on-prem data warehouses with better scalability and no infrastructure management.
The SQL-first cloud data warehouse — fast, scalable and surprisingly affordable when sized correctly.
Tell us what you want to build. We reply within 4 business hours.