Build
Reliable pipelines. Trustworthy data.
We design and operate data lakehouse platforms on AWS and Databricks for organisations whose business reports are delayed because pipelines cannot be relied upon, or whose analysts spend more time cleaning data than analysing it. As a Databricks Delivery Partner and AWS Partner, our recommendations are based on use case, not on vendor preference.
When this is needed
Three signals that this is the right next step.
01 / Pipelines keep breaking
Business reports are consistently delayed because data pipelines cannot be relied upon.
02 / Cleaning instead of analysing
The analytics team spends more time cleaning and fixing data than analysing it.
03 / Data not ready for ML
Data scientists cannot work productively because the data they need is not available in a usable format.
Reference architecture
The shape of a modern data platform.
Illustrative reference architecture. The exact tooling, scale, and integration points are tailored to your organisation’s data sources, regulatory context, and existing investments, we do not deploy a template.
Sources & ingestTransactional databases, SaaS APIs, event streams, files. We use Apache Spark, AWS Glue, dbt, and Apache Airflow for batch and ELT; Apache Kafka and Amazon Kinesis for real-time streaming where the use case requires it.
LakehouseData lakehouse architecture on AWS and Databricks. Layered from raw to cleaned to consumable, so the business serving layer is built on tested, governed data, not on whatever the latest pipeline produced.
ServeOne layer feeding BI, machine learning, and operational integrations. Engineered for measurable reliability, not best-effort delivery.
GovernanceData cataloging, lineage tracking, quality rules, and access control, implemented incrementally, starting from the most critical data domain rather than as a big-bang initiative.