Ingestion and integration
Batch and streaming feeds from ERP, CRM and operational systems, landed once for every consumer.
Data Engineering
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Data & AI
One trustworthy data platform under the reporting.
Most reporting arguments are not about analysis. They are about which extract the number came from, when it was taken and what was filtered out of it.
We build the platform underneath: ingestion from the ERP and the systems around it, a modelled warehouse or lakehouse, and pipelines that run on a schedule with monitoring rather than on someone's laptop.
Modelling is the part that decides whether the platform survives. Conformed dimensions, agreed definitions of customer, product and period, and history kept where the business needs to compare years.
Governance comes with it — lineage, ownership, access control and quality checks — because a warehouse nobody trusts is just another extract with better hardware.
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Solution
What We Put In Place
Ingestion from the applications you run, batch and streaming, landed once and reused by every downstream consumer instead of re-extracted per report.
A modelled warehouse or lakehouse with agreed definitions, slowly changing history and documented lineage from source column to published measure.
Data quality tests and pipeline monitoring, so a broken feed raises an alert before it reaches a board pack.
Modules
Everything The Solution Covers
Source Integration
Batch & Streaming Ingestion
Data Lake & Lakehouse
Warehouse Modelling
Transformation Pipelines
Data Quality Rules
Master Data Management
Lineage & Cataloguing
Access Control & Governance
Pipeline Monitoring
What We Deliver
Data Engineering Capabilities
Modelling
Conformed dimensions, agreed measures and retained history so this year and last year compare properly.
Data quality
Rules, thresholds and reconciliation against source, with failures raised as alerts rather than found in a meeting.
Governance
Ownership, lineage, catalogue and role-based access across the platform.
Outcomes
Outcomes that move your business forward.
We measure success by the impact we create. Here's what good looks like when Data Engineering is running the way it should.
Request a ConsultationOne number with one definition behind it
Agreed measures and conformed dimensions end the argument about which extract a figure came from.
Feeds that raise an alert before a report is wrong
Quality rules and pipeline monitoring catch a broken load rather than letting it reach a board pack.
History kept where the business compares years
Slowly changing dimensions preserve what a record looked like then, so trend analysis survives a master data change.
Reporting that stops re-extracting the same data
Landed once and reused downstream, so a new report is a model change rather than another overnight extract.
Let's talk
Have a technology priority to solve?
Tell us what you are trying to achieve. We will bring together the right capability, technology and delivery model to help move it forward.