Reports take weeks., Trusted data refreshed in minutes, on one governed lakehouse.

Data transformation cleans, joins, governs and reshapes raw operational data into trusted, analytics-ready datasets on a modern lakehouse. Without it, KPIs disagree, dashboards lag by weeks and AI models fail. With it, finance, ops and the board work from one source of truth and new use cases ship in days, not quarters.

Data integration moves data between systems. ETL/ELT extracts and loads it. Data transformation is the 'T' done well, applying business rules, joins, deduplication, conformance and quality tests so the output is usable for analytics and AI. In a modern lakehouse, transformation is code-defined in dbt or Spark, versioned in Git, tested with Great Expectations, and observable end-to-end.

In-house works once you have a senior data platform team, an architect, an SRE on-call rotation and 12+ months of runway. Specialist consultancies compress time-to-value to weeks, bring 2026 reference architectures already proven across banking, energy and government, and transfer knowledge so your team owns the platform after go-live. The hybrid model, consultancy builds, in-house operates, is what most Saudi enterprises now choose.

Most enterprises see a working production slice within 8–12 weeks, hard cost savings (30–50% lower cloud spend, 60–80% faster pipelines) within 6 months, and full payback within 9 months. Revenue impact, faster product launches, better pricing, lower churn, typically materializes in the second and third quarter once business teams trust the data.

We are platform-agnostic across 2026 Magic Quadrant Leaders: Databricks, Snowflake and Microsoft Fabric for the lakehouse; AWS, Azure and GCP for cloud; Informatica, Fivetran, dbt, Airflow, Kafka and Flink for ingestion, transformation and streaming; Great Expectations, Monte Carlo and Purview for quality and lineage. Choice is driven by your existing estate, sovereignty rules and TCO, not vendor preference.

One unified platform handles both. Spark Structured Streaming, Kafka/Confluent and Flink power sub-second use cases (fraud detection, IoT, recommendations), while scheduled batch jobs in dbt or Airflow run finance, regulatory and historical analytics. Same governance model, same lineage, same quality gates, so the business never sees two competing versions of a number.

Yes. We have certified extractors and reference patterns for SAP S/4HANA and BW, Oracle EBS and Fusion, Microsoft Dynamics 365, Salesforce, Mainframe COBOL/DB2, AS/400, legacy data warehouses (Teradata, Netezza, Exadata) and IoT historians (PI, Honeywell). Source systems stay untouched; we land raw data in the lakehouse, transform downstream, then deprecate legacy stores on your timeline.

Every pipeline ships with automated tests (Great Expectations or dbt tests), column-level lineage (OpenLineage, Purview or Collibra), reconciliation against source-of-record systems, and SLA monitoring (Monte Carlo or Databricks Lakehouse Monitoring). Data products only go live once they pass quality gates the business has signed off on, and a dashboard reports freshness, completeness and accuracy in real time.

Yes. We deploy in Saudi sovereign cloud regions (STC Cloud, Oracle Riyadh, Microsoft Azure KSA, AWS Bahrain/Dammam, Google Cloud Dammam) and fully on-premises inside customer data centres. Personal data never leaves the Kingdom, encryption keys stay in customer KMS, and the architecture is reviewed against PDPL, NDMO, SAMA and NCA ECC controls before go-live.

Yes. Pipelines handle Arabic text natively (UTF-8, normalization, diacritics, mixed Arabic/English), Hijri and Gregorian calendars in parallel, Arabic name matching for master data, and RTL-first dashboards in Power BI, Tableau, Looker and Qlik with bilingual measures and labels, used today by Saudi ministries, banks and hospitals.

Modern data transformation directly enables Vision 2030 pillars: a thriving digital economy, government efficiency through shared services, and a data-driven public sector. Our reference architecture maps to SDAIA's National Data Management Office (NDMO) framework, the Data Classification Policy, the Saudi Cloud First policy and the National Strategy for Data & AI, making your platform audit-ready by design.

PII is classified at ingest using NDMO and PDPL taxonomies, then automatically masked, tokenized or encrypted column-by-column. Row- and column-level access controls in Unity Catalog or Snowflake Horizon restrict who can read what, every access is logged, and re-identification requires explicit purpose and approval. Synthetic data is generated for non-production environments.

Our delivery is ISO 9001, 20000, 27001 and 27701 certified. Platform deployments are designed to meet SAMA Cyber Security Framework for banking, NCA Essential Cybersecurity Controls (ECC-1) and Cloud Cybersecurity Controls (CCC-1) for government, NDMO data management standards, PDPL for personal data, HIPAA for healthcare and SOC 2 Type II for cloud workloads, with evidence packs delivered for audit.

A focused engagement runs in three phases: discovery and architecture (3–4 weeks), first production data product (6–8 weeks), scale-out across remaining domains (3–6 months). Most Saudi enterprises see board-visible dashboards live in the same quarter, with the full legacy estate decommissioned inside 12–18 months.

Yes to both. Every engagement bundles role-based training (data engineers, analysts, platform admins, business users), shadowing during build, a knowledge-transfer sprint before go-live, and ongoing 8×5 or 24×7 managed services with SLA-backed response times, covering platform operations, pipeline health, cost optimization and new use case onboarding.

Schemas evolve safely via versioned dbt models, contract tests at the source, schema-on-read in the bronze layer and backwards-compatible silver/gold tables. Breaking changes are caught in CI before they hit production. New business requirements become Git-tracked data products with their own SLAs, so the platform grows continuously without rebuilding from scratch.