Microsoft Data Factory powers data integration across Fabric and Azure with 170+ connectors, Dataflows Gen2, pipelines, and orchestration capabilities. Based in Sydney, Brisbane, and Melbourne, we help Australian businesses build robust ETL/ELT pipelines, migrate from Azure Data Factory, and automate data workflows.
Design and build data pipelines using Data Factory in Fabric and Azure Data Factory. Copy activities, data flows, parameterised pipelines, and 170+ source and sink connectors.
Build low-code data transformations with Dataflows Gen2 powered by Power Query. Mashup queries, staging to Lakehouse, and reusable transformation logic for self-service data preparation.
Orchestrate complex data workflows with triggers, scheduling, dependency chains, and error handling. Event-driven pipelines, tumbling windows, and integration with Azure Logic Apps and Functions.
Migrate Azure Data Factory pipelines and SSIS packages to Data Factory in Fabric. Assessment, refactoring, testing, and parallel running to ensure a smooth transition to the unified platform.
We bring deep Data Factory expertise and hands-on data integration experience to help you build reliable, scalable data pipelines.
Our team are Microsoft MVP level engineers with deep expertise across Azure Data Factory and Data Factory in Fabric, and direct relationships with Microsoft's data platform teams.
We design data pipelines with proper parameterisation, error handling, retry logic, and monitoring so your data integrations are reliable and maintainable.
We work across both Data Factory in Fabric and Azure Data Factory, helping you choose the right platform and migrate between them when needed.
We migrate SSIS packages, Azure Data Factory pipelines, and legacy ETL workloads to Data Factory in Fabric with minimal disruption to your operations.
Trusted by ASX-listed companies and government departments across Australia to deliver data integration projects on time and within budget.
Senior Data Factory specialists based in Sydney, Melbourne, and Brisbane. On-site workshops and face-to-face collaboration when you need it.
Yes. Our Data Factory team is based across Brisbane, Sydney, and Melbourne. We provide on-site workshops, pipeline reviews, architecture assessments, and hands-on implementation support for businesses across Australia.
Azure Data Factory (ADF) is the standalone Azure service for data integration. Data Factory in Fabric is the evolution of ADF built into the Microsoft Fabric platform, with native OneLake integration, Dataflows Gen2, and unified governance. Fabric Data Factory shares the same pipeline concepts but adds Lakehouse destinations, Fabric-native monitoring, and simplified connectivity. We help businesses evaluate and migrate between the two.
ETL (Extract, Transform, Load) transforms data before loading it into the destination. ELT (Extract, Load, Transform) loads raw data first and transforms it in the destination. With Fabric's OneLake and Lakehouse, ELT is often preferred because you can land raw data cheaply and transform it using Spark or SQL. We help you choose the right pattern based on your data volumes, latency requirements, and destination capabilities.
We start with an assessment of your existing ADF pipelines, linked services, datasets, and triggers. We then map each component to its Fabric equivalent, refactor where needed (e.g., switching to OneLake destinations and Dataflows Gen2), and run parallel testing to validate data accuracy. The migration is phased to minimise disruption, with rollback plans at each stage.
Azure Data Factory charges per activity run, data movement, and integration runtime hours. Data Factory in Fabric uses the shared Fabric capacity model (F SKUs). We help you optimise by reducing unnecessary activity runs, right-sizing integration runtimes, using incremental loads instead of full refreshes, and choosing the right platform for your workload profile. Contact our team in Sydney, Brisbane, or Melbourne for a cost assessment.
Get in touch with our Data Factory consultants in Sydney, Brisbane, or Melbourne to discuss your data integration project.
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