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Azure, Data and Security

Data Engineering

Reliable pipelines with quality checks, lineage and monitoring, built to be operated rather than rebuilt.

Technologies

  • Microsoft Fabric
  • Azure Data Factory
  • Azure Databricks
  • Microsoft Purview

Pipelines that fail silently are worse than no pipeline. Quality checks, alerting and lineage make a data platform something an operations team can run.

Business outcomes

Trusted data

Quality checks at ingestion with defined failure handling.

Visible lineage

Any figure traceable to source.

Operable platform

Monitoring, alerting and runbooks for the operations team.

Capabilities

Ingestion design

Batch and streaming patterns with incremental loading.

Transformation

Modelled layers with tested, documented logic.

Data quality

Rules, thresholds and exception handling.

Operations

Monitoring, alerting, runbooks and cost control.

How we deliver it

  1. Assess

    Estate discovery, dependency mapping, criticality and readiness.

  2. Design

    Landing zone, network, identity, policy, cost model and operating cadence.

  3. Build

    Infrastructure as code with policy applied from the first deployment.

  4. Migrate

    Waves sequenced by risk, each rehearsed with a rollback path.

  5. Operate

    Monitoring, cost review, compliance reporting and a platform backlog.

Next step

Considering Data Engineering?

We will give you an honest view of the effort involved, the prerequisites and the risks, before anyone signs anything.

Talk to an expert Solutions