Software · Data and AI
Data warehouses
One governed home for your business data, modelled so analysts and dashboards get the same answer every time.
When finance, sales and operations each keep their own numbers, meetings become arguments about whose figure is right. We design warehouses that bring the data together into clear, documented models: one definition of revenue, of an active customer, of an order. Access rules, partitioning and cost controls are built in from the first table.
What we build
- Warehouse and lakehouse setups on BigQuery, Snowflake, Redshift or ClickHouse
- Dimensional models with facts and dimensions for sales, finance and operations
- A metrics layer with shared definitions of revenue, margin and active customers
- Row and column level security so each team sees only the data it should
- Migrations from on-premise SQL Server or Oracle reporting databases
- Cost dashboards that show query spend by team, user and scheduled job
DemoInteractive · sample data
Try it live.
A working sample with made-up data. It runs in your browser; nothing is sent anywhere.
-- Net revenue by region and month, last 3 months SELECT s.region, DATE_TRUNC(o.order_date, MONTH) AS month, SUM(o.net_amount) AS net_revenue, COUNT(DISTINCT o.customer_id) AS customers FROM marts.fct_orders AS o JOIN marts.dim_store AS s USING (store_id) WHERE o.order_date >= DATE_SUB(CURRENT_DATE(), INTERVAL 3 MONTH)
What it does well
One definition
Metrics are defined once in code and reused by every report, notebook and dashboard.
Modelled for questions
Star schemas and wide tables make common questions fast to run and simple to write.
Governed access
Personal and financial columns are masked by role, and every query is logged.
Partitioned tables
Large tables are laid out so queries scan only the dates and regions they need.
Version controlled
All models live in Git, are reviewed like code and deploy through CI.
Open formats
Apache Iceberg or Parquet storage keeps your data portable between query engines.
Questions
Which warehouse platform should we choose?
BigQuery suits teams on Google Cloud with uneven workloads; Snowflake works well when many teams share compute; ClickHouse is fast and economical for event data; PostgreSQL is enough for smaller volumes. We choose based on your data size, skills and budget.
How do we keep warehouse costs under control?
Partition and cluster large tables, load incrementally, set query quotas per team and watch the most expensive scheduled jobs. We set up cost reporting from day one, so a runaway query is noticed within hours rather than at the end of the month.
Can we keep our existing reports while we migrate?
Yes. We rebuild reports on the new warehouse and run them side by side with the old ones, reconciling totals until they match. Only then are users moved across and the old reporting database retired.