Azure Data Factory

Azure Data Factory is Microsoft's serverless data integration service. Instead of writing custom code to pull data out of one system and load it into another, you build pipelines in a visual designer that handles the extraction, transformation, and loading (ETL/ELT) steps as configured activities. It ships with more than 90 built-in connectors covering on-premises databases, SaaS platforms, and other cloud services, so most integration work is wiring together existing connectors rather than writing new ones.

Underneath the visual layer, an integration runtime provides the actual compute that moves and transforms the data, and a managed Spark engine handles the heavier transformation work without you having to provision or tune a cluster yourself. For organizations still running on-premises ETL through SQL Server Integration Services, Data Factory can also rehost those existing SSIS packages directly in Azure — a way to move workloads to the cloud without a full rewrite.

Every pipeline run is tracked and monitored, with alerting on failures, which matters once you have more than a handful of pipelines feeding a warehouse or lake on a schedule. That combination — broad connectivity, managed transformation, and built-in monitoring — is why it tends to show up early in data warehouse and data lake modernization projects, and in hybrid setups where some systems are still on-premises and others have already moved to the cloud.

How AzureForge AI uses Azure Data Factory

We use Data Factory as the orchestration layer between SQL Server, external APIs, and Cosmos DB — the piece that keeps those systems synchronized on a schedule instead of relying on point-to-point integrations that break every time a source changes. When a client's data lives in a legacy SQL environment but their newer applications need it in a document store like Cosmos DB, Data Factory pipelines are what actually move and reshape it, on a monitored schedule, with failures surfaced instead of discovered days later.