Migrate from Azure Data Factory to Integrate.io: Step-by-Step Guide (2026)

Table of Contents

Since mid-2024, Microsoft has shifted primary development to Fabric Data Factory. New features ship exclusively to Fabric, not ADF. Meanwhile, ADF's consumption billing charges for failed pipeline runs, and forecasting costs can become challenging at scale as orchestration, compute, and data movement charges compound across workloads.

This guide covers the complete migration from Azure Data Factory to Integrate.io: from auditing your existing pipelines to going live in production, including a component-by-component mapping table.

Key Takeaways

Integrate.io covers ETL, ELT, CDC, Reverse ETL, and API Generation under fixed-fee pricing with no per-row, per-run, or per-connector charges.
The migration follows five phases: pipeline inventory, environment setup, pipeline rebuild, parallel run validation, and cutover, typically 4-6 weeks end-to-end.
Integrate.io's 60-second CDC replication replaces ADF's batch-oriented copy activities for near-real-time data movement, fully managed with no self-hosted agent required.
White-glove onboarding includes a dedicated Solution Engineer for the first 30 days, helping rebuild critical pipelines, not just pointing you to documentation.
Teams with multi-cloud environments (AWS + GCP + Azure) gain a cloud-agnostic connector library that works across all three clouds in a single pipeline.

Why Teams Migrate from Azure Data Factory in 2026

Azure Data Factory is a capable orchestration tool inside the Azure ecosystem, but several structural limitations are pushing mid-market data teams toward Azure Data Factory alternatives.

Unpredictable consumption billing

ADF charges across multiple meters simultaneously: pipeline orchestration runs, Data Integration Unit (DIU) consumption, and Data Flow compute. For teams running high-frequency pipelines or data flows, these charges compound in ways that are challenging to forecast before going to production.

Azure ecosystem lock-in

ADF is designed first and foremost for Azure-native resources. Connecting to AWS S3, GCP BigQuery, or non-Microsoft SaaS tools is possible but involves friction: limited connector depth, additional configuration overhead, and self-hosted Integration Runtime requirements for on-premises sources.

Batch-first architecture

ADF is primarily a batch-oriented tool. Teams that need sub-minute change data capture or near-real-time warehouse loading have to bolt on additional services, adding complexity to what should be a single pipeline workflow.

Limited low-code transformation layer

Building complex transformation logic in ADF typically requires either Mapping Data Flows or pushing transformations into a separate tool like dbt. There is no built-in drag-and-drop transformation library comparable to what low-code platforms provide.

Uncertain roadmap

As of 2026, Microsoft's new capabilities (mirroring, copy jobs, and other data integration features) are being built exclusively into Microsoft Fabric Data Factory, not backported to ADF. Teams not migrating to Fabric face a tool with a decelerating investment curve.

If any of these resonate, the migration process below applies directly to your situation.

Phase 1: Audit Your Existing ADF Environment

Migration starts with a complete inventory of what you currently have. Skip this phase and you will discover orphaned pipelines mid-cutover.

Export Your ADF ARM Templates

Azure Data Factory stores all pipeline definitions, linked services, datasets, and triggers as Azure Resource Manager (ARM) templates. Export the full ARM template from the Azure portal:

Navigate to your ADF instance in the Azure portal
Select Author & Monitor → Manage → ARM Template
Click Export ARM Template (this downloads a JSON file with your entire ADF configuration)

The ARM export gives you a machine-readable inventory of every pipeline, dataset, trigger, and linked service in your factory.

Categorize Pipelines by Type

Open the ARM template export and categorize each pipeline into one of three buckets:

Batch copy pipelines - Move data from source to destination on a schedule (these map directly to Integrate.io's Database Replication jobs)
Transformation pipelines - Apply business logic, joins, or data shaping before loading (these map to Integrate.io's Transform & Sync with the visual transformation editor)
Orchestration-only pipelines - Chains of activities that call other pipelines or trigger external processes (these become job dependencies and scheduling rules in Integrate.io)

Document Connected Sources and Destinations

For each pipeline, record:

Source system (Azure SQL, Blob Storage, Salesforce, on-premises SQL Server, etc.)
Destination system (Azure Synapse, Snowflake, Redshift, BigQuery, etc.)
Run frequency (hourly, daily, trigger-based)
Approximate row volume per run
Any SLA or downstream dependency (e.g., "this pipeline must complete before the BI dashboard refreshes at 6am")

This documentation drives your prioritization in Phase 3.

Identify CDC Requirements

Flag any pipeline currently using ADF's self-hosted Integration Runtime or reading transaction logs for near-real-time data movement. These are your CDC candidates. Integrate.io's Database Replication handles these with 60-second latency, fully managed, without a self-hosted agent requirement.

Phase 2: Set Up Your Integrate.io Environment

Provision Your Account

Start your free trial at integrate.io or talk to an expert to provision your environment. Integrate.io assigns a dedicated Solution Engineer during the 30-day onboarding window. Schedule that kickoff call during this phase, not after you've started rebuilding pipelines.

Connect Your Sources and Destinations

Integrate.io's connector library covers 150+ sources and destinations. For a typical ADF migration, the first connections to configure are:

Your primary database sources (Azure SQL Database, Azure Synapse, SQL Server, PostgreSQL, MySQL)
Your cloud data warehouse destination (Snowflake, Redshift, or BigQuery, or Azure Synapse Analytics)
Any SaaS sources that ADF was pulling from via REST connectors (Salesforce, NetSuite, HubSpot)

Each connector uses a simple form-based authentication flow: OAuth for SaaS tools, JDBC connection strings for databases. No SHIR agent to install or manage.

Configure Credentials and Environment Settings

Set up:

Workspace permissions - Assign user roles (Admin, Editor, Viewer) to match your team's access control model
Notification settings - Connect Slack or email for pipeline failure alerts
Scheduling timezone - Set your workspace timezone to match your ADF trigger schedule baseline

Phase 3: Rebuild Pipelines in Priority Order

Rebuild pipelines in three waves, starting with the lowest risk and highest learning value.

Wave 1: Simple Batch Copy Pipelines (Days 1-5)

Start with straightforward batch copy pipelines: one source, one destination, no transformation logic. These are the quickest wins and let your team learn Integrate.io's interface before tackling complex jobs.

In Integrate.io, a batch copy job uses the Database Replication product:

Select your source connector and authenticate
Choose the schemas and tables to replicate
Select your destination and map the target schema
Set your replication schedule (cron expression or interval-based)
Enable auto-schema mapping (Integrate.io detects schema changes at the source and propagates them to the destination automatically)

Auto-schema mapping is one of the key operational differences from ADF: you do not need to manually update linked service definitions or dataset schemas when the source changes.

Wave 2: Transformation Pipelines (Days 6-15)

For pipelines that included Mapping Data Flow logic in ADF, rebuild them using Integrate.io's Transform & Sync product and the visual transformation editor.

The 220+ drag-and-drop transformations cover the common Mapping Data Flow operations:

Filter - Row-level conditional filtering (replaces ADF's Filter activity)
Join - Inner, left outer, full outer, and cross joins across multiple sources
Aggregate - Group-by with sum, count, min, max, distinct count
Derived Column - Calculated fields using expression syntax
Lookup - Reference table enrichment
Union - Combine rows from multiple streams
Rank and Window - Analytical functions for ordered datasets

Rebuild each transformation pipeline by:

Creating a new Transform & Sync job
Adding your source connection as the data input
Dragging transformation nodes onto the canvas in the same logical sequence as your ADF data flow
Connecting the output to your destination
Configuring scheduling to match your ADF trigger frequency

Wave 3: CDC and Real-Time Pipelines (Days 16-25)

ADF's real-time data movement typically required a self-hosted Integration Runtime connected to your on-premises or cloud databases, reading transaction logs. This was one of the more operationally intensive parts of an ADF setup.

In Integrate.io, CDC is handled by Database Replication with log-based CDC enabled:

Enable log-based replication on your source database (binary logging for MySQL, logical replication for PostgreSQL, CDC for SQL Server)
Configure the Integrate.io source connector with CDC mode
Set your destination to receive incremental change events (inserts, updates, deletes)
Integrate.io polls the transaction log every 60 seconds with no SHIR agent, no self-hosted infrastructure

For Salesforce-heavy workflows, Integrate.io's Salesforce Sync product provides bidirectional sync as a purpose-built alternative to ADF's Salesforce linked service connector.

Phase 4: Parallel Run Validation

Before cutting over any production pipeline, run Integrate.io and ADF in parallel for at least 5-7 business days. This parallel period is non-negotiable. Following data migration practices, it's how you catch schema drift, volume discrepancies, and timing dependencies before they become production incidents.

Step 4.1: Define Validation Queries

For each replicated table, write a set of validation queries to run against both your ADF-fed destination and your Integrate.io-fed destination:

-- Row count comparison

SELECT COUNT(*) FROM adf_schema.orders;

SELECT COUNT(*) FROM integrateio_schema.orders;

-- Null rate comparison on key fields

SELECT

SUM(CASE WHEN customer_id IS NULL THEN 1 ELSE 0 END) * 100.0 / COUNT(*) AS null_pct

FROM integrateio_schema.orders;

-- Max updated_at timestamp comparison (confirms recency)

SELECT MAX(updated_at) FROM adf_schema.orders;

SELECT MAX(updated_at) FROM integrateio_schema.orders;

Run these queries after each pipeline execution cycle during the parallel period.

Validate SLA Timing

Check that Integrate.io’s pipeline completion timestamps fall within the same windows your downstream consumers expect. If your BI dashboards query data at 6:00 AM, confirm that Integrate.io's scheduled jobs complete before that window.

Resolve Discrepancies

Common discrepancies during parallel runs:

Row count mismatch - Usually a filter condition difference or a timezone offset in incremental date filters. Check the WHERE updated_at > ? clause in both tools.
Null fields - Typically a data type mapping difference. Review Integrate.io's auto-schema mapping output and adjust type overrides if needed.
Timestamp lag - CDC pipelines may show a 1-2 minute lag vs. ADF batch. This is expected; document the lag and confirm it meets downstream SLAs.

Phase 5: Cutover and Decommission

Once parallel validation passes cleanly for 5-7 days across all pipelines, you're ready to cut over.

Schedule a Cutover Window

Pick a low-traffic window (typically weekend evening) for the final cutover. Notify downstream teams (BI users, operations staff, any systems that consume your pipeline outputs) at least 48 hours in advance.

Disable ADF Triggers

On cutover night:

In the Azure portal, navigate to ADF → Author & Monitor → Manage → Triggers
Stop all triggers (this prevents ADF from running any more pipeline executions)
Let any currently-running pipeline executions complete before proceeding

Activate Integrate.io Pipelines

In Integrate.io:

Confirm all pipeline schedules are active and set to the correct frequency
Trigger a manual run of each critical pipeline and confirm successful completion
Verify destination data is current using your validation queries

Monitor for 48 Hours

Keep ADF triggers disabled but leave the ADF instance running for 48 hours post-cutover. If a critical issue surfaces, you can re-enable ADF triggers as a rollback while you investigate.

After 48 hours of clean Integrate.io operation, you can pause or delete the ADF instance.

Decommission ADF Resources

Once the migration is confirmed stable:

Delete unused Linked Services and Datasets in ADF
Remove Self-Hosted Integration Runtime agents from any on-premises servers
Archive the ARM template export from Phase 1 as historical documentation
Cancel or downgrade the ADF instance based on your Azure subscription model

Common Migration Mistakes to Avoid

Skipping the parallel run phase

Teams eager to decommission ADF often cut this phase short. A single undetected row-count discrepancy in a financial reporting pipeline discovered on a Monday morning is far more expensive than the extra week of parallel billing.

Rebuilding orchestration-only pipelines as pipeline logic

Some ADF pipelines are pure orchestration: they call other pipelines, check conditions, and handle retry logic. In Integrate.io, these become scheduling rules and job dependency chains, not separate pipeline objects. Map them to Integrate.io's scheduling and dependency features rather than trying to recreate them 1:1 as Transform & Sync jobs.

Ignoring the ADF Integration Runtime tail

Self-hosted Integration Runtime nodes on on-premises servers continue running until you explicitly uninstall the agent. Add this to your cutover checklist.

Not involving the dedicated Solution Engineer early

Integrate.io's 30-day onboarding includes a dedicated Solution Engineer. Teams that book this resource during Phase 2 get pipeline builds reviewed and optimized from the start. Teams that call them during Phase 4 when something breaks are using the resource reactively.

Final Verdict

The migration decision from Azure Data Factory to Integrate.io is straightforward for specific situations.

If your primary driver is unpredictable billing, Integrate.io's fixed-fee pricing eliminates ADF's consumption billing entirely.
If you're running multi-cloud (AWS + GCP + Azure simultaneously), Integrate.io's cloud-agnostic connector library is a structural improvement. ADF is designed for Azure-native movement first.
If you need near-real-time CDC without a self-hosted agent, Integrate.io's 60-second log-based replication removes the operational overhead of ADF's Self-Hosted Integration Runtime.
If you're heavily invested in the Microsoft ecosystem (Azure DevOps CI/CD, SSIS packages, Fabric migration plans), our guide to Microsoft ETL tools covers the full stack. Staying on ADF or moving to Fabric Data Factory is the lower-disruption path.
If you're facing the ADF-to-Fabric migration decision, leaving the Microsoft data stack now avoids a second migration later, and Integrate.io's white-glove onboarding covers the transition complexity.

If predictable pricing, multi-cloud flexibility, and a managed migration path are your priorities, Integrate.io is a strong platform to migrate to from Azure Data Factory.

Frequently Asked Questions

Does Integrate.io connect to Azure Synapse Analytics?

Yes. Integrate.io connects to Azure Synapse Analytics as both a source and destination. You can replicate data from Azure SQL, Azure Blob Storage, or any other supported source directly into Synapse, or use Synapse as a source for Reverse ETL workflows that push enriched data back into operational systems.

Can Integrate.io replace ADF's Self-Hosted Runtime?

Yes. Integrate.io connects to on-premises databases (SQL Server, Oracle, PostgreSQL, MySQL) using secure tunneling, without requiring a self-hosted agent installed on your on-premises infrastructure. This removes a significant operational maintenance burden that ADF's SHIR model imposes.

What happens to ADF pipelines during parallel runs?

ADF continues running normally during the parallel phase. All triggers stay active, and pipelines execute on their existing schedules. You run Integrate.io jobs in parallel against a separate destination schema (or a staging schema) and compare outputs. ADF only gets disabled at final cutover.

Is Integrate.io's CDC compatible with SQL Server?

Yes. Integrate.io's Database Replication product supports log-based CDC for SQL Server using SQL Server's native CDC capabilities. You enable CDC on the SQL Server source, grant Integrate.io read access to the change tables, and configure the connector with no additional agents or middleware required.

Will Integrate.io work if our data stays in Azure?

Yes. Integrate.io is cloud-agnostic but works seamlessly with Azure-hosted infrastructure. Your data can move between Azure SQL, Azure Synapse, and Azure Blob Storage entirely within Azure, or you can branch out to AWS, GCP, or multi-cloud destinations as your stack evolves without changing your pipeline configuration.

What support is included during the migration?

Integrate.io’s 30-day onboarding includes a dedicated Solution Engineer who helps configure connectors, review pipeline designs, and troubleshoot issues. Ongoing support includes a 2-minute average first response time and access to the Solutions team for pipeline reviews.

What is an alternative to Azure Data Factory in 2026?

Integrate.io is an alternative to Azure Data Factory for teams that need predictable fixed-fee pricing, multi-cloud flexibility, and built-in CDC without managing self-hosted infrastructure. Integrate.io bundles ETL, ELT, CDC, Reverse ETL, and API Generation under a single license, replacing ADF's consumption billing with a fixed monthly spend that does not scale with run frequency or data volume. For teams staying in the Microsoft ecosystem, Fabric Data Factory is the native upgrade path.

Can I migrate to Microsoft Fabric Data Factory instead?

Yes. Microsoft launched a migration assistant from ADF to Fabric Data Factory in public preview in March 2026, providing an assessment-first approach that automates pipeline conversion for supported types. That path makes sense if your team is deeply embedded in Microsoft's ecosystem: Power BI, OneLake, or Azure Synapse Analytics. If your stack spans multiple clouds (AWS, GCP, Azure simultaneously) or you need predictable fixed-fee pricing, a cloud-agnostic platform like Integrate.io avoids the risk of a second migration when Microsoft eventually fully shifts ADF users to Fabric.

Data Integration