Azure and Databricks consulting for the UAE, Saudi Arabia, Qatar and wider MENA

Data quality you can prove, not just hope for

Metadata-driven data quality frameworks that catch, explain and fix bad data before it reaches a report.

Overview

This is our specialty. We build data quality and remediation frameworks directly into your Databricks pipelines, so every critical record is validated, every failure has a reason and an owner, and every fix is recorded for audit.

Sound familiar?

  • Finance and BI figures do not match and month-end becomes a reconciliation exercise
  • Data issues are discovered by executives instead of by the data team
  • Validation rules are hard-coded in notebooks and nobody knows what is checked
  • There is no record of what was wrong, who fixed it or when

What we deliver

Profiling and baselining

Measure completeness, validity, uniqueness and consistency to show where quality really stands today.

Metadata-driven rule library

Validation rules stored as configuration, not code, so new checks are added without redeploying pipelines.

Exception management

Failed records are quarantined with a reason, severity and owner rather than silently dropped or passed through.

Reconciliation controls

Source-to-target, ERP-to-ledger and layer-to-layer checks that prove totals match before reports are released.

Root cause and remediation

Structured workflows to trace issues to source data, master data or transformation logic, and fix them at the origin.

Quality KPIs and audit trail

Dashboards for pass rates, open exceptions and remediation activity, with history that auditors can follow.

Technology

  • Azure Databricks
  • Delta Lake
  • Lakeflow expectations
  • PySpark
  • Spark SQL
  • Unity Catalog
  • Databricks SQL
  • Power BI

What you can expect

Everycritical data element checked on every load
0extra licences: built natively on your Databricks platform
Fullaudit trail of exceptions, owners and fixes

How we deliver

The same disciplined approach on every engagement, scaled to your scope.

  1. Assess

    Understand your current platform, data and priorities, and agree what success looks like.

  2. Design

    Target architecture and a phased plan, reviewed with your technical and business owners.

  3. Build

    Hands-on delivery with your team, tested and reconciled as we go.

  4. Hand over

    Documentation, runbooks and mentoring so your team owns the result.

Common questions

Anything else? Ask us directly.

Rules such as "amount must be positive" or "SKU must exist in the product master" are stored as rows in a configuration table. The framework reads them at run time, so the business can review, add or change checks without engineers rewriting pipelines.

Usually not. We build the framework natively on Databricks using Delta Lake, Lakeflow expectations and SQL, which avoids extra licences. If you already own a data quality tool, we can integrate with it.

With Critical Data Elements: the handful of fields that drive your most important reports, such as revenue, cost, quantity and customer. We agree them with business owners and build rules for those first.

Checks are designed to run incrementally on new and changed data, so the overhead is typically small compared with the cost of reprocessing or restating reports.

Talk to a senior data architect, not a sales team

A 30-minute call to understand your platform and where it is holding you back. You will leave with practical next steps, whether or not we work together.

Book a consultation