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Case study · Data platforms

Migration capacity without losing your data team's focus

Data platform migrations need extra hands for months, while the internal team still has to keep reports running. This is how we added data engineers who rebuild pipelines on Azure or Microsoft Fabric alongside the client's team and leave the knowledge behind.

Project overview

Data engineers embedded for a migration programme.

Industry
Data platforms
Client
Confidential
Model
Staff augmentation
Skills
Azure Data Factory, Fabric, SQL, Python
Ramp-up
2–4 weeks
Team size
2–5 engineers
Duration
Migration period
Management
The client's data lead
Terms
Monthly

Expected business outcomes

What the client set out to achieve

Migration pace and continuity of existing reporting are the measures.

  • Parallel Migration and business-as-usual

    Extra engineers migrate while the client's team keeps reports running.

    Planned in the client's backlog

  • Validated Every migrated pipeline

    Outputs reconciled against the old platform before switch-over.

    Reconciliation checks per pipeline

  • Documented New platform

    Pipelines and models documented for the client's team.

    Part of the definition of done

Business challenges

Where the client started

A migration on top of the day job.

Platform

On-premises SQL Server and SSIS packages built over years.

Team

Two or three people keeping reports running.

Migration

A cloud migration planned but not resourced.

Risk

Old pipelines nobody fully understands.

Deadline

Server or licence end dates set the timeline.

Proposed solution

How the engagement works

Engineers who migrate systematically and leave knowledge behind.

  • Inventory

    Pipelines, reports and dependencies catalogued and prioritised.

  • Pipeline rebuild

    SSIS and scripts rebuilt in Data Factory or Fabric.

  • Reconciliation

    Outputs compared with the old platform before switching.

  • Report migration

    Power BI reports repointed to the new platform.

  • Documentation

    Pipelines and models documented as they are built.

  • Pairing

    The client's engineers pair on key pipelines to share knowledge.

Project requirements

What the client asked us for

The request was migration capacity. Continuity matters as much: reports must keep working throughout, so every pipeline is reconciled before switch-over.

Knowledge transfer is built in through documentation and pairing, so the internal team owns the new platform.

What the project had to deliver

  • 01Extra migration capacity
  • 02Reports running throughout
  • 03Reconciled outputs
  • 04Knowledge left in the team

Key challenges & considerations

What made it harder, and what we did

  1. Undocumented legacy pipelines

    What we did

    Legacy pipelines were catalogued before anything was moved.

  2. Data volume and refresh windows

    What we did

    Loads were designed to fit the refresh windows.

  3. Security and network setup

    What we did

    Network and security setup was agreed with the client's IT team first.

  4. Business users' time for validation

    What we did

    Validation sessions with business users were booked, and every pipeline was reconciled.

What we watched for

  • Catalogue before migrating
  • Reconcile every pipeline
  • Pair on critical pipelines
  • Document as you build

Development process

Inventory, migrate, reconcile, hand over

Pipelines move in priority order.

  1. Stage 1

    Inventory

    Pipelines and reports catalogued.

  2. Stage 2

    Foundation

    Azure or Fabric environment and standards agreed.

  3. Stage 3

    Migration waves

    Pipelines rebuilt in priority order.

  4. Stage 4

    Reconciliation

    Outputs validated before switch-over.

  5. Stage 5

    Handover

    Documentation and pairing complete.

Technology stack

Microsoft data platform

Azure and Fabric skills matched to the client's target architecture.

Pipelines

  • Azure Data Factory
  • Fabric Data Pipelines

Storage

  • Azure SQL
  • Fabric Lakehouse

Code

  • SQL
  • Python
  • Notebooks

Reporting

  • Power BI

Quality assurance & deployment

Held to the client's own standards

Our engineers worked inside the client's team, so quality was measured the client's way: their definition of done, their reviews and their pipelines.

  • The client's definition of done on every ticket
  • Code review on every change, by the client's team and ours
  • Automated tests written with the work, not after it
  • The client's security and access policies followed
  • Delivery reviewed every sprint with the client's lead

Deployment

  • 01ReconciliationOutputs validated before switch-over.
  • 02HandoverDocumentation and pairing complete.
  • 03Releases through the client's pipelineWork shipped through the client's own CI/CD and release process.
  • 04ContinuityThe same engineers kept on the product, with a monthly review of fit and performance.

Final deliverables

Migrated, and still owned by your team

Pipelines and reports move to Azure or Fabric on schedule, validated against the old platform, while business-as-usual reporting continues.

The client's team understands and owns the new platform when the engagement ends.

  • On-schedule migration
  • Reconciled outputs
  • Continuous reporting
  • Documented platform

Handed over to the client

  • 01Delivered workIn the client's repositories and boards
  • 02DocumentationWritten as the work was done
  • 03Knowledge transferThrough pairing and review
  • 04Monthly reviewsOf delivery and fit
  • 05Flexible termsScale up or down monthly

Success metrics

How success is measured

MeasureOutcomeHow it is measured
Migration and business-as-usual Parallel Planned in the client's backlog
Every migrated pipeline Validated Reconciliation checks per pipeline
New platform Documented Part of the definition of done

Verified reviews

What clients say about our staff augmentation work

Verified reviews from our clients on the same kind of work, published on Clutch.

5.0 27 verified reviews

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