A data engineering company in Saudi Arabia that builds the foundation under your reports and AI

A data warehouse stores structured, modeled tables built for reporting. A lakehouse keeps raw files such as documents, logs and exports alongside those tables, so one platform can serve reports and machine learning. For many mid-size Saudi companies a warehouse is enough, and the honest first question is whether you need one at all. If you have one ERP and a handful of reports, a well-built reporting layer on that system may be the cheaper answer.

When a warehouse is justified, we choose by your constraints, not a favorite brand. Microsoft Fabric, Google BigQuery, Amazon Redshift and open-source stacks such as Postgres with dbt and Airflow can all do the job. What decides it is the skills you already have, where data must be hosted, expected data volume and the licenses you own. We document the choice with the alternatives we rejected and why.

Most warehouse failures are quality failures. A product renamed in the ERP, a customer entered twice, an invoice posted after the month closed: none of these break a pipeline, but all of them break trust. So tests are part of the pipeline. We check for duplicates, nulls, valid ranges and relationships between tables, and we reconcile loaded totals to control figures from finance. A failed check stops the downstream refresh and alerts a named person instead of letting a wrong number reach a dashboard.

We also keep history deliberately. Some tables should show how a customer record looked last quarter, not only today, and that decision has to be made before loading starts. Slowly changing records, late-arriving invoices and corrections all have a defined rule in the design document. The result is a data dictionary your analysts can read, and a pipeline your engineers can change without fear.

Warehouses collect personal data in one place, which makes them useful and risky. The Personal Data Protection Law applies to customer and employee records, so we map which fields are personal, limit who can read them, mask them in analytics layers where they are not needed, and set retention rules. Cloud rules from the Communications, Space and Technology Commission and cybersecurity controls from the National Cybersecurity Authority can shape where the platform runs, and we build the design to the requirements of your sector.

Hosting is a decision with real options: a Saudi data center, a cloud region inside the Kingdom, or your own environment. We plan this with our in-Kingdom hosting service and write the location into the handover. The finished layer feeds BI dashboards, predictive analytics and AI assistants from one source. A first warehouse takes six to twelve weeks, and adding further source systems typically takes two to four weeks each.