The data is already there. The problem is that it is in six different places.
A single source to read from, updated at the frequency the process actually requires.
Data integration does not move data away from the systems that generate it: it collects it regularly in a single location, aligns it, and makes it consistent. From that point on, everyone looking at the same metric sees the same number.
It is the layer everything else relies on — forecasting, alerts, automated responses. Without it, nothing else holds together.
Two collection methods, chosen based on the source
- Scheduled: data extractions at defined intervals. Robust, predictable in terms of resource usage, and easy to rerun when something goes wrong. It is the right choice for most business use cases.
- Real-time: capturing changes as they happen. Necessary only when latency is a process requirement: operational alerts, inventory availability, monitoring.
Beyond a certain scale, updates become incremental: we read only the changes instead of reloading the entire table every night. The benefit is not just speed; it also prevents the source system from being burdened by heavy queries during working hours.
How data is organized once it arrives
- Raw layer: an exact copy of the source, never modified. It is the safety net: if a transformation rule turns out to be wrong, everything else can be rebuilt without requesting the data from the source systems again.
- Normalized layer: clean, typed, deduplicated data, with keys aligned across different systems. This is where transformation rules live, versioned like code.
- Consumption layer: views and aggregates ready for analysis, reporting, and models, optimized for the people using them.
Pipelines you can rerun without fear
We avoid the proliferation of direct connections between systems: the number of integrations grows exponentially, and every change propagates everywhere. The reference architecture includes a mediation layer that isolates the specific characteristics of each system, so replacing an application only affects its connector.
Where systems need to be decoupled, we work with events rather than direct calls, with delivery and ordering guarantees. This allows a slow or temporarily unavailable system to avoid blocking all the others.
Security and traceability
Every data load has a status, a duration, and a count of expected records. If a pipeline does not run, or runs but produces half the usual number of records, an alert is triggered before anyone notices from an incorrect report.
Executions are repeatable without side effects: rerunning a day's data load is a routine operation, not a risky intervention. That is the difference between a pipeline you can manage and one you are afraid to touch.
When the data schema changes
An apparently harmless upstream change — a renamed field, a changed data type — is one of the most common causes of incorrect reports that go unnoticed for weeks. That is why we check incoming schema compatibility: additions are allowed, while incompatible changes are blocked and flagged before they propagate downstream.
When data comes from another department or a supplier, we define in writing what the flow contains, how often it arrives, and who is responsible if it changes. This turns failures into a managed event rather than an unexpected discovery.