Data & Analytics

A data warehouse is where all your business data comes together in one place, structured for querying rather than for transactional operations. Getting the design right matters: a warehouse built on the wrong schema or with the wrong data model becomes slow to query and expensive to maintain as volume grows.nWe design and build data warehouses on Snowflake and BigQuery, selecting the architecture based on your data volume, query patterns, and budget. That includes schema design that makes queries fast, a data model that reflects your business logic accurately rather than mirroring source system structures, and the access control and governance framework that keeps the warehouse trustworthy as more teams use it.

A warehouse is only as reliable as the data feeding it. We build ELT pipelines using dbt, Airbyte, and Fivetran that move data from your source systems into the warehouse on a reliable schedule and with data quality testing built in.ndbt handles the transformation layer: the SQL logic that cleans, normalises, and models raw source data into the tables and views that power your analytics. Metric definitions live in the dbt layer, which means ‘revenue’ means the same thing in every dashboard rather than being calculated differently depending on who built the report. Airbyte and Fivetran handle extraction and loading from source systems, covering hundreds of pre-built connectors and custom connectors for systems that need them.nData quality tests run automatically on every pipeline execution. Failures surface immediately rather than appearing as wrong numbers in a dashboard three days later.

Batch pipelines are sufficient for most reporting needs. For use cases that require data to be acted on immediately, fraud detection, live operational dashboards, real-time inventory, personalisation that responds to user behaviour as it happens, batch is not enough.nWe build real-time streaming pipelines using Apache Kafka and Amazon Kinesis that process events as they occur and make them available for downstream systems with minimal latency. This is the infrastructure that enables a fraud alert to fire before a transaction completes, or a recommendation to update before the user reaches the next page.

Infrastructure that nobody can use produces no value. We build custom dashboards and reporting layers that give business users access to the data they need without requiring them to understand the underlying data model or write SQL.nDashboards are designed around the decisions they need to support, not the data that happens to be available. That distinction matters in practice: a dashboard built around available data shows everything that can be shown, while a dashboard built around a decision shows the two or three numbers that determine what to do next. We work with your team to understand which decisions need to be made and build the views that support them.

Historical reporting tells you what happened. Predictive models tell you what is likely to happen next, with enough lead time to do something about it.nWe build ML models integrated directly into the data warehouse and reporting layer: demand forecasting models that reduce overstock and stockouts, churn prediction that surfaces at-risk customers before they cancel, revenue attribution that shows which channels and campaigns are actually driving conversions, customer lifetime value models that inform acquisition spend, and anomaly detection that flags unusual patterns in operational or financial data before they become problems.