Data Integration
Getting data from where it is produced to where it is used, reliably.
Pipelines that land the right data, on time, with someone told when they do not.
Advisory + implementation. Design first where an estate exists, then build.
Data and platform teams, and the analysts waiting on an extract that arrives when it feels like it.
Reports run on stale data, a nightly load fails silently, or every new source takes a quarter to onboard.
The practice
Source assessment
What each system can give you, how, how often, and what it costs to ask. The constraint is usually access rather than engineering.
Ingestion patterns
Batch, incremental, change data capture or event-driven, chosen against how the data is actually used rather than by preference.
Transformation
Modelled, tested and version-controlled, so a change can be reviewed rather than discovered in a report.
Orchestration
Dependencies, scheduling, retries and backfill, so a late source does not silently produce a half-built day.
Quality gates
Validation that blocks a bad load rather than passing it downstream, and reconciliation that proves the totals still tie.
Monitoring and alerting
Freshness, volume and failure alerts that reach a named owner before the business notices.
Operational handover
Runbooks, documented dependencies and a team that can onboard the next source without us.
Application integration
Where the need is systems talking to each other rather than data landing for analysis, that is systems integration.
Land it, prove it, or say so loudly.
- 01Extract
Batch, incremental, change data capture or event — chosen against how the data is used.
- 02Validate
Quality gates that block a bad load rather than passing it downstream.
- 03Transform
Modelled, tested and version-controlled, so a change is reviewed rather than discovered.
- 04Serve
With a stated freshness promise: how current it is, and how often it lands.
Not the load that fails loudly at three in the morning — that one is fine. It is the one that completes with a third of the rows, because nothing downstream can tell until someone notices a number looks light.
We design and build the pipelines: ingestion pattern, transformation, orchestration, quality gates that block a bad load, monitoring wired to a named owner, and a documented handover to the team who will run them.
We plan for access, because that is where schedules go
Getting credentials, network routes, a service account that does not expire, and someone in the source system’s team who will answer a question. Plan for that and the schedule holds. Ignore it and the first sprint disappears into ticket queues.
Fail loudly, in the right direction
A pipeline that fails visibly at three in the morning is a good pipeline. The bad one is the one that completes with a third of the rows, because nothing downstream can tell the difference until someone notices a number looks light.
Freshness is a promise, so state it
Every dataset should carry what it promises: how current it is, how often it lands, and what happens when it does not. Most arguments about data trust are really arguments about an unstated expectation.
What you keep
The code and the data·Your existing relationships·Approval and control·The ability to stop·The off switchWhat that means
Start with a 45-minute briefing.
No pitch. We’ll map your situation against what actually works and tell you honestly where to start.