Clear, owned outputs.
- ADF architecture and standards
- Metadata-driven ingestion framework
- Integration runtime set-up
- Monitoring and alerting
- CI/CD pipelines for ADF deployment
Azure Data Factory is Microsoft's cloud service for orchestrating data movement and transformation. It connects to many sources, including on-premises systems through self-hosted integration runtimes. We design ADF solutions that are metadata-driven, parameterized, monitored and deployed through CI/CD rather than edited by hand in production.
The problems Azure Data Factory solves.
Metadata-driven ADF pipelines, integration runtimes, monitoring and CI/CD deployment.
Hundreds of near-identical pipelines are copied and pasted.
Changes are made directly in the production factory.
On-premises connections are unreliable.
Failures are only noticed when someone checks the monitor.
What Azure Data Factory covers.
Pipeline design
Reusable, parameterized pipelines and datasets.
Metadata-driven ingestion
One framework that loads many tables from configuration.
Integration runtimes
Self-hosted and managed runtimes for on-premises and private sources.
Transformation
Mapping data flows, or orchestrating Databricks and SQL transformations.
Monitoring and alerts
Azure Monitor alerts and run logging.
CI/CD
Git integration and automated deployment across environments.
How Azure Data Factory is put together.
Azure Data Factory (and the same engine inside Microsoft Fabric and Synapse) is Azure's orchestration and data-movement service. Understanding its building blocks is the difference between a pipeline that runs and one that's maintainable.
We build ADF solutions as code in Git, with parameterised pipelines deployed through CI/CD rather than edited by hand in production.
- Pipelines and activities
- A pipeline is an ordered set of activities — Copy, Data Flow, stored procedure, notebook, web call — with dependencies, branching and retry rules.
- Linked services
- Connection definitions for each source and target: SQL Server, Blob Storage, Dataverse, Salesforce and around a hundred others. Secrets come from Azure Key Vault.
- Datasets
- Named references to the data a linked service holds — a table, a folder of files — parameterised so one dataset serves many tables.
- Integration runtimes
- The compute that runs activities: the Azure IR for cloud-to-cloud, the self-hosted IR to reach on-premises databases securely, and the SSIS IR for lifted SSIS packages.
- Triggers
- Schedule, tumbling-window and storage-event triggers start pipelines; tumbling windows handle backfills and late data cleanly.
- Mapping data flows
- Visual, Spark-backed transformations for joins, aggregations and slowly changing dimensions when SQL in the target isn't the better place.
Why it's worth it.
Less duplication
Metadata-driven patterns replace copy-paste pipelines.
Safer changes
Git and CI/CD replace manual edits in production.
Hybrid reach
Securely connect on-premises and cloud sources.
When Azure Data Factory is the right move.
- Loading SQL Server and Oracle data into an Azure lakehouse.
- Cleaning up an ADF factory that grew without standards.
- Orchestrating Databricks notebooks and SQL transformations.
Technologies & platforms
- Azure Data Factory
- Azure Synapse Pipelines
- Microsoft Fabric Data Factory
- Azure Data Lake Storage
- Azure SQL
- Azure DevOps
- Azure Key Vault
How Azure Data Factory work runs.
- 01
Discover
Sources, consumers, data quality, volumes and the decisions the data must support.
- 02
Design
Architecture, data model, pipelines and governance agreed in writing.
- 03
Build
Pipelines, models and tests delivered in increments.
- 04
Validate
Reconciliation against source systems and sign-off by data owners.
- 05
Operate
Monitoring, data quality alerts, documentation and handover.
Azure Data Factory questions.
Is Azure Data Factory still relevant with Microsoft Fabric?
Yes. Fabric includes Data Factory capabilities, and many organizations run ADF today. We can design with a future move to Fabric in mind.
Can ADF connect to on-premises databases?
Yes, through a self-hosted integration runtime installed inside your network, with secure outbound connectivity.
How do you deploy ADF changes?
With Git integration and automated release pipelines that promote factories between development, test and production environments.
Building pipelines on Azure Data Factory?
Describe the environment and what needs to change. We'll reply with the questions we would answer first and a sensible first step.