IICS Migration Guide: IICS to ADF / Fabric Data Pipelines¶

A comprehensive guide for migrating Informatica Intelligent Cloud Services (IICS) to Azure Data Factory, Microsoft Fabric Data Pipelines, and dbt.

Overview¶

Informatica Intelligent Cloud Services (IICS) is Informatica's cloud-native integration platform. Unlike PowerCenter, IICS is already SaaS-delivered, making the migration conceptually simpler -- you are moving from one cloud service to another rather than from on-prem to cloud. The primary motivations for migration are cost reduction (IPU-based pricing vs consumption-based) and ecosystem unification (consolidating on Azure-native services).

IICS product components¶

IICS is a suite with multiple capabilities. This guide covers:

Architecture comparison¶

graph TB
    subgraph "IICS Architecture"
        IICS_CDI[Cloud Data Integration] --> IICS_Agent[Secure Agent]
        IICS_CAI[Cloud Application Integration] --> IICS_Agent
        IICS_TF[Taskflow] --> IICS_CDI
        IICS_TF --> IICS_CAI
        IICS_Agent --> Source_DB[(Source DB)]
        IICS_Agent --> Target_DB[(Target DB)]
        IICS_Mon[IICS Monitor] --> IICS_TF
    end

    subgraph "Azure Architecture"
        ADF[Azure Data Factory] --> ADF_IR[Integration Runtime]
        LA[Logic Apps] --> APIM[API Management]
        dbt[dbt Cloud / Core] --> Warehouse[(Azure SQL / Synapse)]
        ADF --> dbt
        ADF_IR --> Source_DB2[(Source DB)]
        ADF --> Warehouse
        Monitor[Azure Monitor] --> ADF
    end

Key architectural differences¶

Cloud Data Integration migration¶

CDI task to ADF pipeline mapping¶

Each IICS CDI task maps to an ADF pipeline activity or a combination of dbt model + ADF orchestration:

IICS connector to ADF Linked Service mapping¶

IICS mapping to dbt model conversion¶

IICS visual mappings use a similar concept to PowerCenter but with a cloud-native Spark engine. The conversion process:

Step 1: Export mapping metadata from IICS using the REST API:

# IICS REST API to export mapping definitions
curl -X GET "https://{pod}.informaticacloud.com/saas/api/v2/mapping/{mapping_id}" \
  -H "INFA-SESSION-ID: {session_id}" \
  -o mapping_export.json

Step 2: Analyze transformation logic in the exported JSON and convert to SQL:

Step 3: Create dbt models following the staging/intermediate/marts pattern.

Step 4: Create dbt tests matching any IICS data quality assertions.

Taskflow to ADF pipeline¶

Taskflow concepts¶

IICS Taskflows orchestrate CDI tasks, CAI processes, and other steps. They map directly to ADF pipelines:

Example conversion¶

IICS Taskflow: Daily Sales Load

Start -> Extract_Sales (CDI) -> Transform_Sales (CDI)
    -> Decision (row_count > 0?)
        -> Yes: Load_Warehouse (CDI) -> Notify_Success (Email)
        -> No: Notify_Empty (Email) -> End
    -> On Error: Notify_Failure (Email) -> End

ADF Pipeline equivalent:

{
    "name": "pl_daily_sales_load",
    "activities": [
        {
            "name": "Extract_Sales",
            "type": "Copy",
            "dependsOn": [],
            "typeProperties": {
                "source": { "type": "SqlSource" },
                "sink": { "type": "AzureSqlSink" }
            }
        },
        {
            "name": "Transform_Sales",
            "type": "Custom",
            "dependsOn": [
                {
                    "activity": "Extract_Sales",
                    "dependencyConditions": ["Succeeded"]
                }
            ],
            "typeProperties": {
                "command": "dbt run --select stg_sales int_sales__enriched"
            }
        },
        {
            "name": "Check_Row_Count",
            "type": "IfCondition",
            "dependsOn": [
                {
                    "activity": "Transform_Sales",
                    "dependencyConditions": ["Succeeded"]
                }
            ],
            "typeProperties": {
                "expression": {
                    "value": "@greater(activity('Transform_Sales').output.rowCount, 0)"
                },
                "ifTrueActivities": [
                    {
                        "name": "Load_Warehouse",
                        "type": "Custom",
                        "typeProperties": {
                            "command": "dbt run --select mart_sales"
                        }
                    },
                    {
                        "name": "Notify_Success",
                        "type": "WebActivity",
                        "typeProperties": {
                            "url": "https://logic-app-url/notify-success"
                        }
                    }
                ],
                "ifFalseActivities": [
                    {
                        "name": "Notify_Empty",
                        "type": "WebActivity",
                        "typeProperties": {
                            "url": "https://logic-app-url/notify-empty"
                        }
                    }
                ]
            }
        }
    ]
}

Cloud Application Integration migration¶

IICS Cloud Application Integration (CAI) handles event-driven integration, API orchestration, and process automation. The Azure equivalent is a combination of Logic Apps and API Management:

Migration approach¶

1. Inventory CAI processes -- list all processes, their triggers, and their downstream effects
2. Map to Logic Apps patterns -- most CAI processes translate to Logic Apps workflows 1:1
3. Expose APIs via APIM -- if CAI exposes REST APIs, register them in API Management
4. Set up Event Grid -- for event-driven triggers, use Event Grid as the event bus
5. Test end-to-end -- validate request/response patterns, error handling, retry logic

Data Integration Elastic migration¶

IICS Data Integration Elastic runs large-scale transformations on a Spark cluster. The Azure equivalents are:

When to use Databricks vs dbt¶

Mass Ingestion migration¶

IICS Mass Ingestion handles bulk data loading from multiple sources. The ADF equivalent:

Bulk ingestion pattern¶

{
    "name": "pl_mass_ingestion",
    "activities": [
        {
            "name": "Get_Table_List",
            "type": "Lookup",
            "typeProperties": {
                "source": {
                    "query": "SELECT table_name FROM config.ingestion_tables WHERE active = 1"
                }
            }
        },
        {
            "name": "ForEach_Table",
            "type": "ForEach",
            "dependsOn": [
                {
                    "activity": "Get_Table_List",
                    "dependencyConditions": ["Succeeded"]
                }
            ],
            "typeProperties": {
                "items": {
                    "value": "@activity('Get_Table_List').output.value"
                },
                "isSequential": false,
                "batchCount": 10,
                "activities": [
                    {
                        "name": "Copy_Table",
                        "type": "Copy",
                        "typeProperties": {
                            "source": {
                                "type": "SqlSource",
                                "query": "@concat('SELECT * FROM ', item().table_name)"
                            },
                            "sink": {
                                "type": "ParquetSink",
                                "storeSettings": {
                                    "type": "AzureBlobFSWriteSettings"
                                }
                            }
                        }
                    }
                ]
            }
        }
    ]
}

Intelligent Structure Model migration¶

IICS Intelligent Structure Models (ISM) parse semi-structured data (JSON, XML, logs, EDI) using visual structure detection. ADF Mapping Data Flows provide equivalent capabilities:

Migration approach¶

1. Identify ISM definitions in IICS
2. Map each ISM to an ADF Mapping Data Flow source with appropriate format settings
3. Configure parsing using MDF's built-in flatten, derive, and filter transformations
4. Test with sample data to validate parsing matches ISM output

Monitoring migration¶

IICS Monitor to Azure Monitor¶

Setting up monitoring¶

# Enable ADF diagnostic logging to Log Analytics
az monitor diagnostic-settings create \
    --resource /subscriptions/{sub}/resourceGroups/{rg}/providers/Microsoft.DataFactory/factories/{adf} \
    --name "adf-diagnostics" \
    --workspace /subscriptions/{sub}/resourceGroups/{rg}/providers/Microsoft.OperationalInsights/workspaces/{workspace} \
    --logs '[{"category": "PipelineRuns", "enabled": true}, {"category": "ActivityRuns", "enabled": true}, {"category": "TriggerRuns", "enabled": true}]'

Alerting example¶

# Create alert for pipeline failures
az monitor metrics alert create \
    --name "adf-pipeline-failure" \
    --resource-group {rg} \
    --scopes /subscriptions/{sub}/resourceGroups/{rg}/providers/Microsoft.DataFactory/factories/{adf} \
    --condition "total PipelineFailedRuns > 0" \
    --window-size 5m \
    --evaluation-frequency 5m \
    --action /subscriptions/{sub}/resourceGroups/{rg}/providers/Microsoft.Insights/actionGroups/{ag}

Secure Agent to Self-Hosted IR migration¶

The IICS Secure Agent and ADF Self-Hosted Integration Runtime serve the same purpose: connecting cloud services to on-premises data sources.

Migration steps¶

1. Deploy Self-Hosted IR on the same VMs (or new VMs in the same network) as the Secure Agent
2. Configure network rules -- Self-Hosted IR needs outbound HTTPS to *.azure.com and *.microsoft.com
3. Register data sources as ADF Linked Services using the Self-Hosted IR
4. Test connectivity to each source through ADF
5. Decommission Secure Agent after all IICS tasks are migrated

IICS-specific migration considerations¶

IPU consumption analysis¶

Before migration, analyze your IPU consumption to understand actual usage:

# IICS REST API to get usage metrics
curl -X GET "https://{pod}.informaticacloud.com/saas/api/v2/activity/activityMonitor" \
    -H "INFA-SESSION-ID: {session_id}" \
    -o usage_report.json

Common findings:

• 20-40% of IPUs are consumed by data movement (Copy equivalent)
• 30-50% of IPUs are consumed by transformations (dbt equivalent)
• 10-20% of IPUs are consumed by monitoring and overhead
• 10-20% of IPUs are wasted on failed/retried tasks

IICS parameter and connection migration¶

IICS uses connections and runtime parameters similar to ADF. Create a mapping spreadsheet:

Migration timeline for IICS¶

IICS migrations are typically faster than PowerCenter because:

1. No infrastructure to decommission -- IICS is SaaS; cancel the subscription
2. Mappings are simpler -- IICS promotes simpler, single-purpose tasks
3. Modern patterns -- IICS teams are often already familiar with cloud concepts
4. Fewer legacy workflows -- IICS estates are younger (typically 3-8 years vs PowerCenter's 10-20 years)

Related resources¶

• PowerCenter Migration Guide -- For on-prem PowerCenter migration
• Complete Feature Mapping -- All Informatica features mapped
• Tutorial: Workflow to ADF -- Hands-on walkthrough
• Benchmarks -- Throughput and velocity comparisons
• Best Practices -- Migration execution guidance
• Migration Playbook -- End-to-end migration guide

Last updated: 2026-04-30 Maintainers: CSA-in-a-Box core team