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Stellar TechConnections

Automate and understand

Build the data foundation before the dashboards.

Pipelines, warehouses and models that collect data from your systems, check it and keep it current, so analytics, AI and reporting all start from the same reliable source.
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At a glance

Best for
Organisations with data spread across many systems, where every report starts with a manual export
Typical engagement
[XYZ: confirm]
Typical duration
[XYZ: confirm]
Team
[XYZ: confirm]
Talk to an engineer

The problem

Where this costs you time.

  • Data scattered across systems

    People, payroll, finance and operations each hold their own copy.

  • Exports and spreadsheets

    Every report begins with manual extraction and clean-up.

  • Numbers nobody can trace

    A figure on a dashboard cannot be followed back to its source.

What we do

Capabilities

[XYZ: confirm scope with engineering]

  • Source assessment

    What data exists, where it lives and how reliable it is.

  • Pipelines and ingestion

    Automated, monitored loads from your systems, files and APIs.

  • Warehouse design

    A structure sized to your needs, from a simple warehouse to a larger platform.

  • Transformation and modelling

    Business rules written once, tested, and reused by every report.

  • Data quality and lineage

    Checks at each step, and a way to trace any figure back to its source.

  • Access and governance

    Roles, masking of sensitive fields and a record of who used what.

Benefits

What changes when this works.

Outcomes we design for. Measured results appear only after a client confirms them.

  • One trusted source for reports, analytics and AI
  • Fewer manual exports and clean-ups
  • Figures you can trace to their source
  • A foundation that grows with the questions you ask

Process

How data engineering and warehousing engagements run.

  1. Stage 1

    Discover and Diagnose

    Business, workforce and compliance analysis.

    Tech deliverablesProcess map, data-flow map, risk register

  2. Stage 2

    Design and Align

    Solution architecture, operating models, integration planning.

    Tech deliverablesArchitecture, prototype, test plan

  3. Stage 3

    Implement and Enable

    Deployment, onboarding, system validation, change management.

    Tech deliverablesBuild, integrate, test, train

  4. Stage 4

    Govern and Optimize

    Continuous compliance monitoring, reporting, automation and enhancement.

    Tech deliverablesMonitor, support, improve

Technologies

Tools we would use.

[Confirm the real stack]

  • Storage

    • PostgreSQL
    • SQL Server
    • Data warehouse
    • Object storage

  • Pipelines and modelling

    • dbt
    • Python
    • SQL
    • Orchestration

  • Quality and access

    • Data tests
    • Lineage tracking
    • Role-based access
    • Audit logging

Industries

Where this matters most.

  • Oil and gas

    Workforce, contractor and site data brought together for head office.

  • Pharmaceuticals

    Traceable data with documented sources, ready for audit.

  • Chemicals and fertilizers

    Plant, shift and attendance data consolidated across sites.

Related

Where this connects.

All services

FAQ

Questions we hear.

Answers are drafted for review. Anything that needs a fact from Stellar is marked.

Data and analytics is the reporting people see. Data engineering is the pipelines and storage underneath it. Many projects need both.

Have a business challenge worth solving?

Tell us what you're trying to fix. An engineer will reply, not a form robot.

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