AI-assisted migration to Microsoft Fabric

Move SSIS, ADF & Synapse to Microsoft Fabric.

Multi-agent analysis to validated Fabric assets, with your experts in control.

Migrate to my new data platform See how it works
AI-assisted, never autonomous.
  • No-obligation estate assessment
  • Your code & data stay yours
  • Experts in control at every gate
0
source platforms supported
SSIS · ADF · Synapse
0
specialized agents in a
closed Analyze→Validate loop
minutes
to inventory an estate that
used to take weeks
1
canonical model behind
every tool, every pipeline
The migration tax

Manual migration doesn't scale, and the risk shows up late.

Modernizing a data platform usually means analyzing hundreds of integration pipelines by hand. It's slow, inconsistent, and dependent on a handful of experts who carry the knowledge in their heads. The hardest components surface near the deadline, when they're most expensive to fix.

  • Weeks of manual inventory before a single asset moves
  • Complexity judged by gut feel, not data, so plans slip
  • High-risk packages discovered late, not early
  • Delivery scales linearly with team size, not ambition
  • Key-person risk: lose the expert, lose the migration
Engineers reviewing a complex legacy data estate
Hundreds of pipelines. One deadline. Traditionally, all of it by hand.
From tedious to meta-driven

The same migration, re-engineered as a factory.

The AI engine automatically inventories assets, detects complexity drivers, and recommends the most appropriate Fabric-native patterns, shifting projects from slow, manual work to a scalable, predictable approach.

Traditional migration

  • Estate discoveryManual inventory, weeks of analysis
  • ComplexityExpert judgment, inconsistent
  • Pattern selectionTrial-and-error
  • RiskOften surfaces late in the project
  • PrioritizationSpreadsheet exercises
  • ScalabilityLinear with team size
  • KnowledgeKey-person risk

AI-assisted migration Plainsight

  • Estate discoveryAutomated scanning in minutes
  • ComplexityAI-driven scoring, repeatable
  • Pattern selectionAutomated Fabric recommendations
  • RiskEarly detection of high-risk components
  • PrioritizationData-driven backlog generation
  • ScalabilityFactory-style parallelization
  • KnowledgeCodified, resilient intelligence

Business impact: faster kickoff, predictable planning, reduced rework, fewer surprises, better ROI focus, lower migration cost, and teams that don't break when one expert leaves.

Inside the engine

We read the logic, not just the labels.

Every analyzer parses the native definition. For SSIS that means the package XML inside .dtsx and .ispac: precedence constraints, Data Flow components, variable and parameter scopes, connection managers and embedded T-SQL. For ADF and Synapse it means the Git JSON: activities, dependsOn edges, datasets, linked services and trigger schedules.

Control flow vs data flow

Precedence constraints become a typed DAG; Data Flow components map to discrete transform nodes with row-level semantics.

Expression resolution

SSIS expressions and ADF @-expressions are evaluated to surface hidden parameter and runtime coupling.

Embedded SQL extraction

Execute SQL Tasks, stored-proc calls and source queries are isolated for dialect translation to Fabric T-SQL or Spark SQL.

Lineage reconstruction

Source-to-sink column lineage is rebuilt across staging hops, including SCD and surrogate-key patterns.

Risk indicators

Script Tasks, dynamic SQL, file-system access and unsupported connectors are flagged with a weighted score.

Run semantics

Truncate-load vs incremental, watermark columns and trigger cadence are captured to preserve behavior in Fabric.

DimCustomer.cmm.jsonCanonical Migration Model
{
  "asset": "DWH_Load_DimCustomer",
  "source": {
    "tool": "SSIS",
    "package": "DimCustomer.dtsx",
    "protectionLevel": "EncryptSensitive"
  },
  "controlFlow": [
    { "task": "TRN Staging",   "type": "ExecuteSQL" },
    { "task": "DFT Load",      "type": "DataFlow", "components": 7 },
    { "task": "UPS Dimension", "type": "ExecuteSQL", "scd": "Type 2" }
  ],
  "dataFlow": {
    "source": "OLEDB SRC_CRM.dbo.Customer",
    "transforms": ["Lookup", "DerivedColumn", "SCD"],
    "sink": "OLEDB DWH.dbo.DimCustomer"
  },
  "lineage": ["SRC_CRM.Customer", "STG.Customer", "DWH.DimCustomer"],
  "risk": { "score": 0.62, "drivers": ["ScriptTask", "DynamicSQL"] },
  "complexity": "Medium",
  "confidence": 0.91
}
The core principle

A closed loop: Analyze → Design → Generate → Validate.

Legacy workloads are analyzed for structure and risk, translated into an optimal Fabric design, generated into deployable assets, then validated for correctness. The result is repeatable, high-quality, and always under expert control at the critical decisions.

  1. Analyze

    Reconstruct the functional behavior of each legacy pipeline: control flow, data flow, SQL logic, dependencies and risk indicators.

  2. Design

    Select the optimal Fabric components and apply company standards (naming, medallion mapping, error handling) into a Target Design Spec.

  3. Generate

    Transform the approved design into deployable Fabric assets: pipelines, SQL scripts, notebook scaffolding and configuration templates.

  4. Validate

    Verify the generated solution against the design spec, surface defects early, and produce a report ready for business sign-off.

Modular, multi-agent architecture

Specialized agents, each with one job, handing off cleanly.

Tool-specific analyzers interpret the source logic. Their outputs are normalized into one canonical model that drives automated target design and asset generation for Microsoft Fabric.

Source analysis

SSIS Analyzer

Parses .ispac/.dtsx packages, extracting control and data flows, SQL logic, dependencies and risk indicators like Script Tasks.

ADF Analyzer

Reads ADF Git artifacts, rebuilds the pipeline dependency graph and parameter usage, and identifies sources, sinks and migration risks.

Synapse Analyzer

Analyzes Synapse pipelines and detects workspace-specific coupling, including SQL pool dependencies and hybrid Spark/SQL patterns.

Migration intelligence

Normalization Agent

Converts every analyzer output into one Canonical Migration Model: standardized structure, transforms, parameters and lineage.

Fabric Designer Agent

Determines the optimal Fabric implementation per pipeline and applies your standards into a Target Design Specification.

Build & validate

Fabric Generator Agent

Turns the approved design into ready-to-deploy assets, and can optionally create the Fabric artifacts automatically.

Fabric Validation Agent

Verifies the built solution against the design spec, detects issues early, and produces a validation report for sign-off.

Validated Fabric assets

Analyzed, standardized, designed, generated and validated, ready to promote through DEV → ACC → PRD with your DevOps process.

Pattern to platform

Every source pattern has a deliberate Fabric target.

The Designer Agent does not lift and shift blindly. It maps each detected pattern to the Fabric component that fits its workload, then applies your medallion and naming standards.

Source pattern
Detected in
Fabric target
Data Flow, row transforms
SSIS
Dataflow Gen2 or Notebook (PySpark)
Lookup + Slowly Changing Dimension
SSIS
Notebook MERGE into Delta (Type 1/2)
Execute SQL Task, stored proc
SSIS, Synapse
Warehouse T-SQL procedure
Script Task (.NET / VB)
SSIS
Notebook (Python), flagged for review
Copy activity
ADF, Synapse
Fabric pipeline Copy activity
Mapping Data Flow
ADF
Spark Notebook or Dataflow Gen2
Dedicated SQL pool query
Synapse
Warehouse or Lakehouse SQL endpoint
Tumbling-window trigger
ADF
Fabric pipeline schedule
SQL Server Integration Services◆ Azure Data Factory◆ Azure Synapse Pipelines◆ → Microsoft Fabric◆ SQL Server Integration Services◆ Azure Data Factory◆ Azure Synapse Pipelines◆ → Microsoft Fabric◆

Curious what this looks like on your estate?

Tell us about your SSIS, ADF or Synapse landscape and we'll share our approach, success stories, and how the framework would apply to you.

Start the conversation
High-fidelity outputs

Every step produces an artifact your team can review.

Nothing is a black box. The framework emits structured, inspectable outputs at each control point, from estate scoring to the final validation report.

Complexity scorecard

estate_scan.summary
Low 128 Medium 137 High 47
Dynamic / parameterized SQL 0.31
Script Tasks (.NET) 0.24
Unsupported connectors 0.18
Parameter sprawl 0.14
312pipelines scanned
0.89avg confidence

Target Design Spec

DimCustomer.tds
BRONZElh_raw.crm_customerCopy
SILVERlh_stg.stg_customerDataflow Gen2
GOLDwh_dwh.dim_customerNotebook MERGE
GOLDwh_dwh.usp_upsert_dimT-SQL proc
SCD Type 2, error output to lh_raw.reject

Validation report

run #0428
Schema parity (source vs target) 124/124
Row-count reconciliation delta 0
SCD Type 2 effective dating ok
Null and key integrity ok
Dynamic SQL block review
Naming convention compliance ok
Ready for sign-off, 1 item flagged for review
AI-assisted, not AI-autonomous

Your experts stay in control where it matters.

Agents dramatically accelerate analysis and generation, but human validation and architectural oversight remain mandatory at defined control points, especially for complex business logic and production promotion.

Assessment review

Experts approve the complexity assessment before anything is designed.

Design sign-off

The Target Design Specification is reviewed before assets are generated.

Validation gate

The validation report supports business sign-off before promotion to production.

Plainsight experts reviewing a migration design together
Why teams choose this

Faster time-to-insight. Predictable delivery. Less risk.

Discovery in minutes

Automated estate scanning replaces weeks of manual inventory, so projects kick off faster.

Predictable planning

Consistent, AI-driven complexity scoring turns guesswork into data-driven roadmaps.

Risk surfaced early

High-risk components are flagged at the start, not discovered near the deadline.

Factory-scale delivery

Parallelized, repeatable execution lowers migration cost and decouples speed from headcount.

Tool-agnostic model

One canonical model means consistent outcomes across SSIS, ADF and Synapse alike.

Resilient teams

Codified intelligence removes key-person risk and keeps knowledge in the framework.

Good questions

What teams ask before they start.

Is the migration fully automated?

No. The framework is AI-assisted, not AI-autonomous. Agents accelerate analysis and generation, but human review and approval remain mandatory at defined control points: assessment, design, and validation.

Which source platforms do you support?

The framework supports the core Microsoft data integration ecosystem: SQL Server Integration Services (SSIS), Azure Data Factory (ADF), and Azure Synapse Pipelines, all normalized into one canonical model.

What exactly gets generated?

Deployable Fabric assets aligned to your standards: Data Factory pipelines, Warehouse SQL scripts, notebook scaffolding and configuration templates, optionally created automatically, then validated against the design specification.

How does it handle our company standards?

The Fabric Designer Agent applies your conventions (naming, medallion-layer mapping, and error-handling strategy) when producing the Target Design Specification, so generated assets fit your platform.

How do the assets reach production?

Validated assets are promoted through DEV → ACC → PRD using your organization's existing DevOps process, with the validation report supporting business sign-off.

Getting started

From first call to a migration plan in three steps.

No procurement marathon, no big upfront commitment. We start small, prove the approach on your own estate, and only then scope the full migration.

  1. 01

    Discovery call

    A short conversation about your SSIS, ADF or Synapse landscape and what a successful move to Fabric looks like for you.

    ≈30 minutes · no prep needed
  2. 02

    Estate assessment

    We run the analyzers on a representative slice of your estate and hand back a complexity scorecard and a clear risk map.

    No obligation
  3. 03

    Tailored migration plan

    A prioritized, data-driven roadmap with effort, risk and a predictable, expert-controlled path to Microsoft Fabric.

    Prioritized & risk-ranked

Your IP stays yours

Your packages and pipelines are analyzed for your migration, not used to train public models.

Experts in control

Human review and sign-off are mandatory at assessment, design and validation gates.

Fits your process

Validated assets promote through your existing DEV → ACC → PRD DevOps pipeline.

Book my discovery call No forms. Opens an email to info@plainsight.pro.
Let's talk

Ready to migrate to your new data platform?

Leave your email and we'll share our approach, success stories, and how the Plainsight AI Migration Framework applies to your estate.

Migrate to my new data platform

Opens your email to info@plainsight.pro. No forms, no spam.

Migrate to Microsoft Fabric