STRAX and Azure Data Factory

STRAX and Azure Data Factory, compared.

Azure Data Factory is Microsoft's cloud service for building data pipelines, often feeding analytics platforms in Azure. STRAX keeps operational systems on the same records and runs on your own servers.

At a glance

How the two approaches differ.

Azure Data FactorySTRAX
Where it runsMicrosoft Azure, with a self-hosted runtime to reach on-premises sourcesYour own servers
Main purposeData movement and transformation pipelines, often into Azure analyticsKeeping operational systems on the same records and building on that data
TimingPipelines run on schedules or triggersChanges captured as they happen
DirectionUsually from a source to a destinationBoth ways through the hub
Beyond data movementOrchestrates other Azure data servicesWorkflows, portals, reports, an API gateway and monitoring on the same data

Analytics pipelines and operational systems

Azure Data Factory is built for data engineering at scale: copying and transforming data into lakes and warehouses where analysts work with it. It fits naturally when the rest of the data platform is in Azure.

STRAX works on the operational side. It keeps the systems that run the business on the same customers, orders and assets, continuously and in both directions, and it runs entirely on infrastructure you control.

When Azure Data Factory is the better fit

  • The goal is analytics, loading data into Azure Synapse, Data Lake or a similar platform.

  • Your organisation has standardised on Azure and its data services.

  • The work is large-scale data engineering run by a dedicated team.

When STRAX is the better fit

  • Data must stay on your own servers instead of a public cloud.

  • Operational systems must agree on the same records all day, in both directions.

  • You want portals, workflows and reports on the same data without another project.

See it on your own systems.

A demo takes about an hour, on systems like yours, so you can judge the fit for yourself.