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Solution

Enterprise Data Platform.

A governed, SaaS-delivered lakehouse: ingestion and change data capture, a Bronze/Silver/Gold medallion architecture on ACID storage, catalog and automated lineage, data quality, master data, and BI, AI, and GenAI on top. Built to satisfy NDMO, NCA, and PDPL from day one, not bolted on before the audit.

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Time to trusted data
≤ 24 hrs
Pipeline reliability (SLA)
99.9%
Legacy platform cost
−30 to −55%
Governance posture
NDMO-aligned

The condition

A data lake that became a data swamp, and a governance mandate with no platform under it.

The familiar pattern in large enterprises and government entities: a Hadoop-era data lake that nobody fully trusts, two or three warehouses with conflicting versions of the truth, batch jobs that fail silently and surface three weeks later as a wrong dashboard, no column-level lineage when a regulator asks where a number came from, no golden record for customer or asset, and a data-classification policy that lives in a document rather than in the platform. Meanwhile the mandate has moved: NDMO now expects documented lineage, classification, and data-quality evidence; NCA expects the cybersecurity controls around it; and PDPL expects lawful, minimized, protected handling of personal data. The data is not the problem. The engineering and governance around it is.

An Enterprise Data Platform closes that gap as one engineered platform rather than a shelf of disconnected tools. We ingest current and future sources with change data capture and event-driven streaming, land them in a medallion lakehouse on ACID storage with time-travel, wrap the whole estate in catalog, automated lineage, data quality, and master data management, secure it with classification, RBAC and ABAC, masking, tokenization, and DLP, and expose it through secure APIs, BI, self-service analytics, and governed AI and GenAI. Platform and cloud engineering, AI and ML, and security and compliance move together, so the first use case ships on a substrate the next ten reuse.

What success looks like

Outcomes you can track monthly.

Every enterprise data platform engagement publishes a metrics dashboard at kickoff and updates it monthly. No vanity metrics, no mystery ROI.

Time to trusted data
≤ 24 hrs
Pipeline reliability (SLA)
99.9%
Legacy platform cost
−30 to −55%
Governance posture
NDMO-aligned

Practice mix

3 practices, orchestrated against one outcome.

Solutions are not single-practice engagements. The roles below show how each practice contributes, the same way a delivery plan names owners and acceptance criteria.

  1. 01

    FOUNDATION + SKYWAY

    Platform & Cloud

    Cloud architecture, DevOps, SRE, migrations, data engineering.

    Role here

    Owns the lakehouse foundation: ingestion and CDC, event-driven streaming, the Bronze/Silver/Gold medallion layers on Delta Lake or Apache Iceberg, migration off the legacy data lake, and the secure API layer.

    Open the practice
  2. 02

    CORTEX

    AI & Machine Learning

    Generative AI, agents, computer vision, predictive analytics, and MLOps, engineered for production.

    Role here

    Delivers the value layer on governed data: BI and self-service semantic models, the feature store, and GenAI enablement such as retrieval-augmented answers and text-to-SQL over trusted, permission-aware data.

    Open the practice
  3. 03

    GUARDIAN + CITADEL

    Quality & Security

    Test automation, performance, accessibility, application security, secure SDLC.

    Role here

    Authors the governance and security frame: data catalog and lineage, quality scorecards, classification, RBAC and ABAC, masking and tokenization, DLP, audit logging, and the NDMO, NCA, and PDPL control mapping.

    Open the practice

How we engage

5 phases, orchestrated across practices.

Each phase has named owners across the practices listed above, a shared deliverable, and an acceptance criterion at the program (not the squad) level.

  1. 01

    Assessment and target architecture

    Inventory current sources, the legacy lake and warehouses, and the data-governance maturity against NDMO and NCA expectations. Output is a target lakehouse architecture, a source and CDC plan, a governance and security control map, a migration path with reconciliation strategy, and a cost model. Fixed-price, typically four to six weeks.

  2. 02

    Ingestion, CDC, and streaming foundation

    Stand up connectors for current and future sources, log-based change data capture from SAP, Oracle, SQL Server, and DB2, event-driven near-real-time pipelines on Kafka, Kinesis, or Event Hubs, and automated file ingestion with checksum and integrity validation that quarantines bad files rather than corrupting the lake. Everything lands in the Bronze layer with provenance intact.

  3. 03

    Medallion lakehouse and migration

    Build the Bronze, Silver, and Gold layers on Delta Lake or Apache Iceberg with ACID transactions, time-travel, and schema and version management, each promotion governed by an explicit contract. Migrate the legacy data lake with row-level reconciliation and a documented, reversible cutover so nothing is lost and every number is traceable to source.

  4. 04

    Governance, quality, and master data

    Deploy the data catalog, automated column-level lineage, and business glossary; author data-quality rules, scorecards, issue management, and monitoring dashboards; build master data management and golden records across the domains that matter; and apply classification, RBAC and ABAC, masking, tokenization, DLP, and audit logging. Map every control to NDMO, NCA ECC, and PDPL so the evidence is a one-day pull, not a six-week scramble.

  5. 05

    Analytics, AI, and operate

    Expose the Gold layer through a governed semantic layer for BI and self-service analytics, a feature store for AI and ML, and GenAI patterns such as retrieval-augmented answers and text-to-SQL over permission-aware data, plus secure APIs and an API gateway for application and government-system integration. Hand off with runbooks and a shadow period, or keep us on as Managed Services for production operations.

Capabilities

What’s in scope.

Capabilities span all the practices contributing to this solution. Out-of-scope items are named in the SOW too.

  • SaaS-delivered lakehouse on Databricks, Microsoft Fabric, or Snowflake, or a self-managed open stack
  • Ingestion and connectivity for current and future sources, batch and streaming, with an extensible connector catalog
  • Change data capture from SAP, Oracle, SQL Server, and DB2 (log-based, Debezium, Fivetran, or Qlik)
  • Event-driven, near-real-time processing on Kafka, Kinesis, Event Hubs, Flink, or Spark Structured Streaming
  • Automated file ingestion with checksum and integrity validation and quarantine on failure
  • Medallion lakehouse: Bronze, Silver, and Gold layers with explicit promotion contracts
  • ACID storage, time-travel, and schema and version management on Delta Lake or Apache Iceberg
  • Legacy Data Lake to lakehouse migration with row-level reconciliation and reversible cutover
  • Data catalog, metadata management, and automated column-level lineage (Unity Catalog, Purview, Collibra, OpenMetadata)
  • Data-quality rules, scorecards, issue management, and monitoring dashboards
  • Master Data Management and golden-record creation across customer, asset, and reference domains
  • Secure APIs and API gateway for application and government-system integration
  • BI and self-service analytics on a governed semantic layer (Power BI, Tableau, embedded)
  • AI, ML, and GenAI enablement on governed data: feature store, retrieval-augmented answers, text-to-SQL
  • Data-layer security: encryption, RBAC and ABAC, classification, masking, tokenization, DLP, audit logs, private connectivity
  • Governance mapped to NDMO (NDMG), DGA, NCA ECC, and KSA PDPL and SDAIA for Saudi engagements

Industries

Where enterprise data platform lands.

Most-frequent buyer industries. Each card opens the industry-scoped playbook with sector-specific compliance and operating constraints.

Selected work

Quantified outcomes, not adjectives.

All case studies

Common questions

Asked before the first call.

  1. 01

    Is this a product you sell, or a platform you build on ours?

    It is an engineered platform delivered on a SaaS lakehouse foundation you own, typically Databricks, Microsoft Fabric, or Snowflake, or a self-managed open stack (Spark, Delta or Iceberg, Kubernetes) when data residency or cost demands it. We are platform-agnostic. We design the architecture, build ingestion, medallion layers, governance, and security, migrate you off the legacy lake, and hand you an operating cadence. The lakehouse licenses are yours; the engineering and governance are what we deliver.

  2. 02

    How do you handle change data capture from SAP?

    Log-based CDC wherever the source supports it, so we read committed changes without loading the transactional system. For SAP that is typically the database transaction log via a connector such as Qlik Replicate, Fivetran HVR, or Debezium, or SAP-native operational data provisioning where the landscape requires it. The same pattern covers Oracle, SQL Server, and DB2. Changes land in Bronze with ordering and provenance preserved, then promote through Silver and Gold under explicit contracts.

  3. 03

    Why a lakehouse and medallion architecture rather than a warehouse?

    A lakehouse gives you warehouse-grade reliability (ACID transactions, time-travel, schema enforcement) on open table formats (Delta Lake or Apache Iceberg) over cheap object storage, so BI, data science, and GenAI all read the same governed tables instead of copies drifting apart. The Bronze, Silver, and Gold medallion layers make data quality and trust explicit: raw and immutable in Bronze, conformed and validated in Silver, business-ready and governed in Gold. Every consumer knows which layer they are reading and what guarantees it carries.

  4. 04

    How does the platform satisfy NDMO, NCA, and PDPL?

    We map the platform controls to the specific frameworks rather than claiming a certificate we do not hold. For NDMO we deliver the data-management domains it expects: catalog and metadata, automated lineage, data-quality scorecards, classification, and master data. For NCA we engineer the Essential Cybersecurity Controls around the platform: identity, encryption in transit and at rest, network segmentation, private connectivity, logging, and monitoring. For PDPL and SDAIA we build lawful, minimized, permission-aware handling of personal data with masking, tokenization, and DLP, plus data residency inside the Kingdom where required. The output is procurement-grade evidence, not a slide.

  5. 05

    What data-layer security controls are included?

    Encryption in transit and at rest, RBAC and ABAC for coarse and attribute-based access, automated data classification and labeling, dynamic data masking and tokenization for sensitive fields, DLP policies, comprehensive audit logging, and private connectivity (PrivateLink, private endpoints, or in-Kingdom private networking) so data never traverses the public internet. Access is governed at the platform layer, so self-service analytics and GenAI inherit the same policies rather than reimplementing them per tool.

  6. 06

    Can you migrate our existing data lake without downtime or data loss?

    Yes. We run migration in waves with row-level reconciliation between the legacy lake and the new lakehouse, so every table is proven equivalent before cutover. Consumers move over per domain with a documented, reversible rollback at each wave. Time-travel on the new platform means that even after cutover you can query historical state and audit exactly what changed and when.

  7. 07

    How do BI, self-service analytics, and GenAI fit on top?

    The Gold layer publishes a governed semantic model, so BI tools (Power BI, Tableau, or embedded analytics) and self-service users work from consistent, permission-aware definitions rather than hand-rolled SQL. On the same governed data we enable AI and ML through a feature store and GenAI through retrieval-augmented answers and text-to-SQL that respect row and column permissions, so a business user can ask a question in natural language and only ever see data they are entitled to.

Talk to us

Bring an enterprise data platform mandate. We’ll bring a senior engineer.

A senior engineer plus the practice leads who’d staff this program join the first call. No discovery gauntlet, no junior reps.

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