Azure Data Factory development
Azure Data Factory Pipeline Development
Reliable, monitored data pipelines between your systems, databases and data platform.
Azure Data Factory is Microsoft's cloud service for moving and transforming data. We design and build Data Factory pipelines, connect on-premises and cloud sources, add monitoring and CI/CD, and help you decide when Fabric Data Factory is the better home for new work.
What we cover
- Pipeline and activity design
- Self-hosted integration runtimes
- Incremental and change-based loads
- Mapping data flows
- Git integration and CI/CD
- Monitoring and alerting
Requirement-led advice · Documented handover
- Advice based on your requirements
- Works with your existing stack
- Documented, maintainable delivery
What Is Azure Data Factory?
Azure Data Factory (ADF) is a managed data integration service. Pipelines group activities that copy data, run transformations, call stored procedures, notebooks or APIs, and control the flow with conditions, loops and error handling. Linked services and datasets describe connections to more than ninety sources, from SQL Server and SAP to Salesforce, files and REST APIs.
Integration runtimes decide where work runs: the Azure runtime for cloud sources, a self-hosted runtime installed on your network for on-premises systems, and an Azure-SSIS runtime for existing SSIS packages. Triggers run pipelines on schedules, time windows or events. Microsoft now develops much of its new integration work in Fabric Data Factory, which uses a similar pipeline model inside Microsoft Fabric.
When Azure Data Factory is a good fit
- You need scheduled or event-driven data movement in Azure.
- Some sources sit on-premises behind a firewall.
- You have existing SSIS packages to run in the cloud.
- You already run ADF pipelines and want them more reliable or easier to change.
What Teams Use Data Factory For
Common pipeline patterns we build and maintain.
Database replication
Copy operational data into a warehouse or lake.
Incremental loads
Load only new and changed rows using watermarks or change tracking.
On-premises extraction
Move data securely from systems behind your firewall.
SaaS and API ingestion
Pull data from CRM, ERP and REST APIs.
SSIS lift and shift
Run existing SSIS packages on Azure-SSIS.
Orchestration
Coordinate notebooks, stored procedures and other jobs.
File processing
Ingest CSV, Excel, JSON and Parquet files on arrival.
Operational monitoring
Alerts and dashboards for failed or late pipelines.
Azure Data Factory Services We Deliver
Each piece of work has a clear output, so you know what you will receive.
Integration design
Map sources, targets, schedules and load patterns.
Deliverables: integration design, source inventory
Pipeline development
Parameterised, reusable pipelines with error handling.
Deliverables: pipelines, documentation
Runtime setup
Self-hosted integration runtimes with high availability.
Deliverables: runtime setup, network notes
CI/CD
Git integration and automated deployment between environments.
Deliverables: repository, release pipeline
Monitoring
Alerts, logging and run dashboards.
Deliverables: alert rules, dashboard, runbook
Modernisation
Review existing factories or plan a move to Fabric Data Factory.
Deliverables: review report, migration plan
Data Factory Components
Building blocks we configure.
Orchestrate
- Pipelines and activities
- Triggers
- Parameters and variables
- Control flow and error handling
Move and transform
- Copy activity
- Mapping data flows
- Stored procedures and notebooks
- Change data capture
Connect
- Linked services and datasets
- Azure integration runtime
- Self-hosted integration runtime
- Azure-SSIS integration runtime
Operate
- Git integration
- ARM-based CI/CD
- Azure Monitor alerts
- Managed identities and Key Vault
Listing a feature here does not mean every project uses it. We recommend what fits your requirements, skills, licensing and running costs.
How Data Factory Fits Your Stack
Data Factory commonly moves data from SQL Server, Dataverse and SaaS systems into Microsoft Fabric, Azure SQL or Databricks, where PySpark notebooks handle heavier transformation.
Pipelines are deployed through Azure DevOps or GitHub. For dedicated engineers, see Hire ETL Developers and Hire Data Engineers.
Implementation Considerations
- Parameterise pipelines so one pattern serves many tables.
- Use incremental loading instead of full reloads wherever the source allows.
- Run self-hosted runtimes on at least two nodes for production.
- Store secrets in Azure Key Vault and use managed identities.
- Watch data-flow cluster costs; use the copy activity where it is enough.
- For new platforms on Fabric, consider Fabric Data Factory from the start.
How We Work With Azure Data Factory
The same four steps we use across our technology work, adapted to your scope.
Discover
Goals, users, constraints and the systems Azure Data Factory must work with.
Select
Confirm Azure Data Factory is the right fit and agree the architecture and scope.
Build and integrate
Deliver in short iterations with testing, reviews and documentation.
Operate and improve
Monitor, support and improve the solution as your needs change.
Azure Data Factory Questions, Answered
What is Azure Data Factory?
A managed Azure service for building pipelines that move and transform data between cloud and on-premises systems.
Can Data Factory reach on-premises databases?
Yes, through a self-hosted integration runtime installed inside your network.
Can we run SSIS packages in Data Factory?
Yes, with the Azure-SSIS integration runtime, which helps when moving existing SSIS work to the cloud.
What is the difference between ADF and Fabric Data Factory?
They share a similar pipeline model. Fabric Data Factory runs inside Microsoft Fabric with its own connectors and dataflows. We help you choose for each workload.
How do you deploy Data Factory changes?
Through Git integration and release pipelines that promote changes between environments.
How do you know when a pipeline fails?
We add alerts, logging and dashboards so failures and delays are noticed quickly.
Building or Fixing Data Pipelines?
Tell us about your sources, targets and schedules, and we will suggest an approach.
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