AI-Assisted Migration

Intro¶

This guide describes how AI agents can accelerate and standardize migrations from SQL-based integration tools to Microsoft Fabric. The approach is designed to industrialize repeatable migration work while maintaining strong human oversight and delivery quality.

⚠️ Important: This is an AI-assisted migration approach and is not fully autonomous. Human review and approval remain mandatory at defined control points.

AI-Driven Migration¶

AI-assisted migration brings structure, speed and risk reduction to large-scale data platform modernizations. Instead of manually analyzing hundred of integration pipelines, the AI engine automatically inventories assets, detects complexity drivers and recommends the most appropiate Fabric-native implementation patterns. This shifts migration projects from slow and tedious work to a scalable meta-driven approach. By using this approach there will be faster time-to-insight, more predictable delivery and significantly reduced migration risk.

Capability	Traditional Migration	AI-Assisted Migration	Business Impact
Estate discovery	Manual inventory, weeks of analysis	Automated scanning in minutes	Faster project kickoff
Complexity assessment	Expert judgment, inconsistent	AI-driven scoring	Predictable planning
Migration pattern selection	Trial-and-error	Automated Fabric recommendations	Reduced rework
Risk identification	Often late in project	Early detection of high-risk components	Fewer surprises
Portfolio prioritization	Spreadsheet exercises	Data-driven backlog generation	Better ROI focus
Delivery scalability	Linear with team size	Factory-style parallelization	Lower migration cost
Knowledge dependency	Key-person risk	Codified intelligence	More resilient teams

To realize these benefits in practice, the AI-assisted migration capability is implemented through a modular agent framework that supports the major SQL-based integration tools.

Supported integration tools¶

The AI-assisted migration framework is designed to support the core Microsoft data integration ecosystem through a unified, metadata-driven approach. The agents can ingest and analyze legacy SSIS projects as well as modern cloud-native Azure Data Factory and Azure Synapse definitions. Their control flow, data movement and transformation logic will be nomalized into a common model. Based on this model, the AI engine generates migration insights, complexity scoring and recommends fabric-native implementations. After a human review of the assessment an automatic generation of the Fabric artifacts could take place.

SQL Server Integration Services (SSIS)
Azure Data Factory (ADF)
Azure Synapse Pipelines

Core Principles of the AI-Driven Migration Framework¶

The AI-assisted migration framework operates as a continuous lifecycle of Analyze → Design → Generate → Validate. Legacy workloads are first analyzed to understand structure and risks, then translated into an optimal Fabric design, automatically generated into deployable assets, and finally validated to ensure correctness and reliability.

This closed-loop approach enables scalable, repeatable, and high-quality migrations while keeping human experts in control at critical decision points.

flowchart LR
  A[Analyze] --> D[Design]
  D --> G[Generate]
  G --> V[Validate]
  V --> A

AI-Driven Migration Framework¶

This section introduces the AI-driven migration framework used to accelerate and standardize the transition of SQL-based integration workloads to Microsoft Fabric. The framework applies a modular, multi-agent approach in which specialized AI agents collaborate to analyze legacy solutions, design the optimal Fabric architecture, and generate production-ready assets.

The design emphasizes:

Repeatability — consistent outcomes across projects
Scalability — ability to process many pipelines efficiently
Transparency — clear hand-offs between agents
Human control — mandatory review points remain in place

Each agent has a single, well-defined responsibility. Tool-specific analyzers first interpret the source logic (SSIS, ADF, Synapse). Their outputs are normalized into a unified canonical model, which then drives automated target design and asset generation for Microsoft Fabric.

⚠️ Important: This framework is AI-assisted, not AI-autonomous. While agents significantly accelerate analysis and generation, human validation and architectural oversight remain essential to ensure correctness, performance, and business alignment.

The diagram below illustrates the end-to-end interaction between the agents in the migration pipeline.

flowchart TD
  %% =========================
  %% Inputs
  %% =========================
  subgraph INPUTS[Source Inputs]
    I1[(SSIS Project)]
    I2[(ADF Git Repository)]
    I3[(Synapse Git Repository)]
  end

  %% =========================
  %% Analysis Layer
  %% =========================
  subgraph ANALYSIS[Source Analysis]
    A1[SSIS Analyzer Agent]
    A2[ADF Analyzer Agent]
    A3[Synapse Analyzer Agent]
  end

  %% =========================
  %% Intelligence Layer
  %% =========================
  subgraph INTEL[Migration Intelligence]
    N[Normalization Agent]
    D[Fabric Designer Agent]
  end

  %% =========================
  %% Build & Validate Layer
  %% =========================
  subgraph BUILD[Build & Validation]
    G[Fabric Generator Agent]
    H[Fabric Validation Agent]
  end

  %% =========================
  %% Output
  %% =========================
  O[(Validated Fabric Assets)]

  %% Flow
  I1 --> A1
  I2 --> A2
  I3 --> A3

  A1 --> N
  A2 --> N
  A3 --> N

  N --> D
  D --> G
  G --> H
  H --> O

Below is a concise explanation of each part of the AI-driven migration framework.

Source Inputs¶

These are the starting artifacts that contain the legacy integration logic.

SSIS Project — .ispac or .dtsx packages representing existing ETL workflows.
ADF Git Repository — JSON definitions of pipelines, datasets, linked services, and triggers from Azure Data Factory.
Synapse Git Repository — Synapse pipeline and related artifacts extracted from the workspace repo.

Purpose: Provide the authoritative source logic that the analyzers will interpret.

Source Analysis¶

This layer contains tool-specific analyzer agents that reconstruct the functional behavior of the legacy workloads.

SSIS Analyzer Agent¶

Parses SSIS packages to extract control/data flows, SQL logic, dependencies, and risk indicators (e.g., Script Tasks). It produces a structured logical view of each SSIS pipeline.

ADF Analyzer Agent¶

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

Synapse Analyzer Agent¶

Analyzes Synapse pipelines and detects workspace-specific dependencies such as SQL pool coupling or hybrid Spark/SQL patterns. It produces a structured logical model similar to the other analyzers.

Outcome of this layer: Each legacy tool is translated into a structured, comparable representation.

Migration Intelligence¶

Normalization Agent¶

This agent converts the tool-specific analyzer outputs into a single Canonical Migration Model (CMM). It standardizes pipeline structure, transformation types, parameters, and scheduling information.

What it enables:

Tool-agnostic downstream processing
Consistent pattern classification
Complexity and confidence scoring
Unified lineage representation

Fabric Designer Agent¶

Determines the optimal Microsoft Fabric implementation for each pipeline. It selects the appropriate Fabric components (e.g., Data Factory pipeline, Warehouse SQL, Notebook) and applies company standards such as naming, medallion mapping, and error-handling strategy.

Outcome: One normalized view of the migration workload, regardless of the original technology. Proposed Target Design Specification (TDS) describing how the solution should look in Fabric.

Build & Validate¶

Fabric Generator Agent¶

Transforms the approved design into deployable Fabric assets. It generates pipelines, SQL scripts, notebook scaffolding, and configuration templates aligned with project standards.

Outcome: Ready-to-deploy Fabric implementation artifacts. Optionally automatically creates the artifacts.

Fabric Validation Agent¶

Verifies that the generated Fabric solution compared to the TDS. Detects issues in the the solution that is built.

What it enables:

Migration confidence
Early defect detection
Business sign-off support

Outcome: Validation report and approved Fabric assets.

Final Output¶

Validated Fabric Assets¶

The end result is a set of Fabric pipelines and related artifacts that have been:

analyzed
standardized
designed
generated
and validated

These assets are ready for promotion through DEV → ACC → PRD using the organization’s DevOps process.

⚠️ Reminder: The framework is AI-assisted. Human review and approval remain essential, especially for complex business logic and production promotion.