Data & Cloud
We design and engineer cloud data platforms that unify batch and streaming data, enforce quality and governance, and give analytics and AI teams trusted data to build on.
Governance plane — across every layer
Capabilities
Reliable batch and streaming pipelines built as maintainable, tested code.
Lakehouse and medallion designs that balance cost, performance and flexibility.
Platform foundations on AWS, Azure and GCP with infrastructure as code.
Lakehouse implementation, Delta Lake, Unity Catalog and workload optimisation.
Distributed processing tuned for large-scale transformation workloads.
Event streaming and near real-time ingestion for operational data.
Ingestion and transformation patterns matched to each source and use case.
Dimensional models and serving layers that analysts can trust.
Automated expectations, validation and quarantine at every layer.
Access control, cataloguing, classification and policy enforcement.
Freshness, volume and schema monitoring with actionable alerts.
End-to-end lineage from source to dashboard to model.
Use cases
Move from fragmented warehouses and data lakes to a unified lakehouse with a medallion architecture.
Capture operational events in near real time for analytics, alerting and personalisation.
Introduce catalogue, access policies and lineage without slowing delivery teams down.
Engagement pattern
A representative engagement pattern for this solution area.
Data & Cloud
Explore more
Generative AI, RAG and agentic systems engineered for enterprise reliability, security and control.
Data science, machine learning, model training and LLM fine-tuning — from experiment to monitored production model.
From business intelligence to decision intelligence — analytics that changes what people do next.
Customer data platforms, identity resolution and activation that turn data into personalised experiences.
FAQ
A lakehouse combines the low-cost, flexible storage of a data lake with the reliability and performance of a data warehouse, using open table formats such as Delta Lake. It lets BI, machine learning and generative AI work from one governed copy of data instead of several disconnected systems.
The medallion architecture organises data in three layers: bronze holds raw, replayable source data; silver holds cleansed, validated and conformed data; gold holds business-ready, modelled data for analytics and AI. Each layer adds quality checks, making issues easier to trace and fix.
Both. We recommend a platform based on your workloads, existing skills, cloud and cost profile. Databricks suits heavy data engineering, streaming and machine learning; Snowflake suits SQL-centric warehousing. Many organisations use them together.
Quality and governance are built into pipelines from the start: automated expectations and quarantine at each layer, access control and classification through a catalogue such as Unity Catalog, and end-to-end lineage and observability so teams can see freshness, volume and schema changes.
Let's explore what you can build with your data.
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