EverydayUser storyConsultative playbook

Our integration platform is on its third generation and still painful

The data team is maintaining a tangle of SSIS jobs, custom Python scripts, and ageing ETL tooling. New integrations take weeks. Fabric Data Factory and Logic Apps offer a cleaner pattern but adoption needs to be phased without breaking existing workloads.

Trigger: Integration backlog growing; legacy ETL tooling at end-of-life.
Good outcome: Fabric Data Factory live for new workloads; legacy ETL migrated in waves; lineage continuous via Purview.

Diagnostic discovery

Signals this story fits

Observable cues that confirm the conversation belongs here.

·SSIS estate aging; new integrations taking weeks
·Heavy reliance on custom Python or Bash scripts
·Tool fragmentation — three or more integration platforms
·Pipeline lineage and observability informal
·Integration tooling cost growing without commensurate value

Questions to ask

Open-ended, SPIN-style — each one has a reason it matters.

1.What is the current integration estate — SSIS, ADF, Logic Apps, Boomi, MuleSoft, custom?
WhySizes the platform sprawl. Most customers have three or more.
Listen for: “too many to count” · “each team picks their own” · “historical reasons”
2.How many integration patterns are live — batch, micro-batch, event-driven, streaming?
WhySurfaces the platform-fit dimension. Different patterns need different tools.
3.What is the throughput and volume profile per integration?
WhyDetermines whether Fabric Data Factory absorbs the workload or specialised tooling is needed.
4.Who owns each integration today, and what is the support model?
WhyOften nobody owns the legacy SSIS jobs; they run because they ran yesterday.
5.What is your CI/CD posture for integrations?
WhySource control + deployment is the maturity threshold. Surfaces engineering practice.
6.What is your lineage coverage today across the integration estate?
WhyWithout lineage, every audit becomes archaeology.

Baseline → target architecture

TOGAF-style gap framing — what we typically see today, and what the proposed end state looks like. The gap between them is the engagement.

Baseline architecture

Mix of SSIS, ad-hoc Python/Bash scripts, sometimes Boomi or MuleSoft. Integration code in fragmented repositories. Manual deployment. Pipeline monitoring uneven. Lineage informal. New integrations take weeks because the platform decision is re-litigated every time.

Typical concerns

·SSIS estate aging; engineering interest declining
·Multiple integration platforms with overlapping capability
·No unified lineage across pipelines
·Deployment manual; rollback painful
·Cost of integration tooling growing without value

Capability gaps

·Strategic integration platform with unified governance
·Event-driven patterns for the right workloads
·CI/CD for pipelines
·Cross-platform lineage
·Unified monitoring

Target architecture

Fabric Data Factory as the strategic integration platform — batch and micro-batch workloads, native OneLake integration, unified with the rest of Fabric. Logic Apps for event-based and SaaS connector scenarios. Azure Functions for serverless transformations. Source control + CI/CD for pipelines. Purview Data Governance provides cross-pipeline lineage. Unified monitoring via Azure Monitor.

Key capabilities

Strategic integration platform on Fabric
Event-driven integrations via Logic Apps
CI/CD for pipelines
Cross-platform lineage via Purview
Unified pipeline observability

Enabling SKUs

Resolved in the ‘Recommended cards’ section below.

Architecture decisions

Each decision is offered as explicit options with trade-offs — Hohpe's “selling options” principle. A safe default is noted where one exists.

Decision 1.Strategic platform — Fabric Data Factory vs standalone Azure Data Factory vs Synapse Pipelines
Fabric Data Factory
When it fitsFabric is the strategic data platform; OneLake-integrated integrations.
Trade-offsTied to Fabric capacity.
Azure Data Factory (standalone)
When it fitsNon-Fabric estate; pure integration use case.
Trade-offsLess native lineage; separate billing.
Synapse Pipelines
When it fitsAlready invested in Synapse; not yet migrated to Fabric.
Trade-offsSame engine as ADF, but Synapse on a long deprecation curve.
Default recommendationFabric Data Factory for new integrations and SSIS migration target. Standalone ADF only where Fabric is not yet in scope.
Decision 2.Migration approach — parallel run vs replace per workload
Parallel run
When it fitsCritical integrations where validation matters more than speed.
Trade-offsTwo pipelines running for weeks; reconciliation cost.
Replace per workload
When it fitsNon-critical or low-risk integrations; cutover acceptable.
Trade-offsCutover risk; rollback path needed.
Default recommendationParallel run for tier-1; replace for tier-2 and tier-3.
Decision 3.Pattern selection — Data Factory (batch) vs Logic Apps (event) vs Functions (serverless transform)
Data Factory for batch + micro-batch
When it fitsScheduled or volume-driven workloads; ETL/ELT patterns.
Trade-offsLatency bound to schedule.
Logic Apps for event-driven + SaaS connectors
When it fitsSaaS integrations (Salesforce, ServiceNow); event triggers; low-code surface.
Trade-offsPer-execution cost at scale.
Azure Functions for serverless transforms
When it fitsCustom transformation logic; engineering team comfortable with code.
Trade-offsOperational overhead for monitoring and lifecycle.
Default recommendationData Factory as the default; Logic Apps for SaaS and event triggers; Functions for custom transforms only.

Low-risk trial — proof of value

8-week integration platform pilot

8 weeks

Fabric Data Factory provisioned in the existing Fabric capacity. Five SSIS jobs migrated via parallel run. Two event-driven integrations stood up in Logic Apps. Source control + CI/CD configured for the new pipelines. Purview catalogues the new and reconciled pipelines with lineage.

Success criteria

Five SSIS jobs migrated with parity validated against parallel run
Two event-driven integrations in production via Logic Apps
CI/CD pipeline deploying integrations on commit
Purview lineage live for the migrated and new integrations

InvestmentConsumed against existing Fabric capacity. Purview Data Governance consumption ~€2–4k/month for the trial scope. Legacy SSIS continues running during parallel run.

Proof metrics

·Time-to-deliver-new-integration reduced by 50%+
·SSIS migration velocity measured per quarter
·CI/CD adoption above 80% on new integrations
·Purview lineage coverage above 70% on the migrated workloads

Recommended cards

The SKUs and capabilities most likely to be part of the solution, with the editorial rationale for each in the context of this story. Add the ones that fit your situation.

SKUMicrosoftSaaS

Microsoft Fabric

Microsoft's unified SaaS analytics platform — Data Engineering, Data Warehouse, Real-Time Analytics, Data Science, Power BI, Data Factory, OneLake (the unified storage layer), Activator (real-time triggers). Capacity-based pricing across all workloads. The Microsoft strategic answer to the platform-fragmentation question.

In: Azure consumption

Data & Analytics

Why for this story

Fabric Data Factory is the strategic integration platform — integrated with OneLake, unified with the rest of Fabric, and the destination for SSIS modernisation. Capacity-based pricing replaces per-platform licensing.