Data engineering with Microsoft Fabric

Microsoft Fabric is the foundation CloudLab Latam uses to unify company information. The data engineering service designs ingestion, transformation, storage, and the rules that stop sales, finance, and operations from keeping private copies of the same fact. What the business sees is often a Power BI dashboard, but reliability is created earlier: in repeatable pipelines, a shared model, and one definition of customer, product, and period.

Many organizations arrive with manual extracts. Someone downloads the ERP on Friday, someone else pastes columns into Excel, and a third person fixes customer names by hand. That method does not scale and leaves no trace when a figure changes. On Fabric we replace that chain with a flow that can be run again, with a record of what arrived, what was transformed, and what was rejected. The data team stops being the bottleneck of every report request.

The design starts from use. The entire ERP database is not copied on day one if the urgent question is margin and receivables. The necessary entities are chosen, the grain is agreed, and a minimum quality bar is written down: required fields, duplicates, impossible dates, and codes that do not exist in the catalog. Those rules run in the pipeline, not in an analyst comment. What fails is set aside and can be reviewed, instead of being mixed quietly into the rest.

Integration covers the sources that actually move decisions: ERP, CRM, spreadsheets that are still the system of record, bank files, supplier APIs, and operational events. Each connector has a schedule, an owner, and a behavior when it fails. If a source did not arrive, the dashboard must not show a zero that looks like a real drop in sales. That operational detail is what separates an abandoned data lake from a platform the business uses.

Fabric is planned together with Power BI and, when it applies, with Power Automate. The pipeline leaves the table ready; the semantic model exposes the metrics; a flow can warn if a quality check fails. Separating those layers means a change in the source does not break ten different reports. It also means a business analyst can add a measure without rewriting ingestion. CloudLab Latam makes that boundary explicit in the solution documentation.

Information governance is part of the deliverable. We define where sensitive data lives, who can read the lakehouse, how each table is named, and how access is requested. The company data-processing policy, aligned with Colombian regulation and with the standards the client must already meet, becomes concrete technical choices: what is masked, what is kept, and for how long. It is not a legal annex disconnected from the architecture.

Knowledge transfer is practical. The internal team walks through a pipeline, sees where to change a rule, and knows who to tell if a source changes shape. Not everyone in the company needs to be a data engineer, but more than one person must be able to operate the platform. We leave diagrams, conventions, and a first set of controls that can grow.

A first data domain, with priority sources and a clear path to Power BI, can be running in a two-to-four-week cycle when the scope is cut well. If every area still has its own version of the truth, request a free consultation and we will choose the domain that hurts most: sales, finance, inventory, or another that is already slowing decisions. Bring one painful report and its source. We will tell you whether Fabric is the right first step or whether a smaller Power BI model is enough for now. That keeps the first release small enough to operate.