Our Data Factory Expertise

ETL/ELT Pipeline Development

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.

Dataflow Gen2 & Transformations

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.

Data Orchestration & Automation

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.

ADF to Fabric Migration

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.

Technologies We Work With

Data Factory

  • Data Factory in Fabric
  • Azure Data Factory
  • Dataflows Gen2
  • Data Pipelines
  • Copy Activity & Data Flows

Data Sources

  • SQL Server & Azure SQL
  • REST APIs & OData
  • SharePoint & Dynamics 365
  • Amazon S3 & Redshift
  • SAP, Oracle & Snowflake

Fabric & Azure

  • OneLake & Lakehouse
  • Fabric Data Warehouse
  • Azure Key Vault
  • Azure Monitor & Alerts
  • Azure DevOps & Git

Why Choose Team 400 for Data Factory

We bring deep Data Factory expertise and hands-on data integration experience to help you build reliable, scalable data pipelines.

Microsoft MVP-Led

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.

Pipeline Architecture Experts

We design data pipelines with proper parameterisation, error handling, retry logic, and monitoring so your data integrations are reliable and maintainable.

Fabric & ADF Specialists

We work across both Data Factory in Fabric and Azure Data Factory, helping you choose the right platform and migrate between them when needed.

Migration Specialists

We migrate SSIS packages, Azure Data Factory pipelines, and legacy ETL workloads to Data Factory in Fabric with minimal disruption to your operations.

Proven Enterprise Delivery

Trusted by ASX-listed companies and government departments across Australia to deliver data integration projects on time and within budget.

Local Australian Teams

Senior Data Factory specialists based in Sydney, Melbourne, and Brisbane. On-site workshops and face-to-face collaboration when you need it.

Frequently Asked Questions

Do you have Data Factory consultants in Brisbane and Sydney?

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.

What is the difference between Azure Data Factory and Data Factory in Fabric?

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.

What is the difference between ETL and ELT, and which should we use?

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.

How do you approach migrating our existing ADF pipelines to Fabric?

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.

How is Data Factory priced and how can you help optimise costs?

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.

Ready to Streamline Your Data Integration?

Get in touch with our Data Factory consultants in Sydney, Brisbane, or Melbourne to discuss your data integration project.

Contact Us