Azure Data Factory

Orchestrate Your Data With Confidence

Azure Data Factory (ADF) is Microsoft’s cloud-based data integration service that helps you create, schedule, and manage data pipelines at scale. It enables organisations to bring data from multiple sources together, transform it efficiently, and make it available for analytics and decision-making.

Discover what’s possible with Azure Data Factory

What Is Azure Data Factory?

Azure Data Factory is a fully managed, serverless data integration platform. It allows you to design and automate data workflows without managing infrastructure. With ADF, you can connect to on-premises and cloud data sources, transform raw data into usable formats, and deliver it into destinations such as Azure Synapse, Microsoft Fabric, or Power BI.

What Can You Do With Azure Data Factory?

Azure Data Factory provides powerful tools to manage the entire lifecycle of data movement and transformation.

Data ingestion at scale: Bring data from hundreds of on-premises and cloud sources into a centralised environment.
ETL and ELT processing: Build Extract, Transform, and Load (ETL) or Extract, Load, and Transform (ELT) pipelines with low-code or no-code interfaces, as well as support for complex coding.
Real-time and batch processing: Support both scheduled batch data loads and near real-time processing to meet different business needs.
Hybrid connectivity: Seamlessly integrate on-premises systems with cloud destinations through secure gateways.
Integration with analytics platforms: Deliver prepared data directly into services such as Azure Synapse, Microsoft Fabric, and Power BI for reporting and advanced analytics.

Core Components of Azure Data Factory

Microsoft Fabric: The End-to-End Platform

ADF combines several capabilities that make data integration easier and more scalable.

Pipelines: Logical groupings of activities that define the sequence of data movement and transformation.
Data Flows: Visual tools for designing transformations such as joins, aggregations, and lookups without writing code.
Linked Services: Connections to data sources and destinations, such as SQL databases, data lakes, or SaaS platforms.
Integration Runtime: The compute infrastructure that executes activities, available as cloud-hosted or self-hosted.
Monitoring and management tools: Dashboards and alerts to track pipeline health and performance.

Advantages of Azure Data Factory

Azure Data Factory delivers significant value by simplifying complex data integration across diverse systems and providing a unified platform for orchestrating, transforming, and managing data at scale.

Serverless and fully managed

No infrastructure to maintain. ADF automatically scales resources to match workload demands, from simple pipelines to complex enterprise jobs.

Broad connectivity

With native connectors to over 100 cloud and on-premises sources, ADF enables you to integrate diverse systems, eliminate silos, and unify data.

Cost efficiency

ADF uses a pay-as-you-go model. You only pay for pipeline execution and data movement, avoiding upfront infrastructure costs.

Integration with Microsoft ecosystem

Works seamlessly with Azure Synapse, Microsoft Fabric, Databricks, Power BI, and more, allowing true end-to-end analytics solutions.

Enterprise-grade security

Built-in encryption, role-based access, and compliance with global standards keep sensitive data safe and governed.

Together, these advantages make Azure Data Factory a cost-effective, secure, and scalable choice for modern data integration.

Optimisation and Best Practices

To get the most value from Azure Data Factory, organisations should follow established best practices for design and governance.

Pipeline design: Break complex processes into modular, reusable pipelines for easier maintenance.
Performance tuning: Use parallelism and partitioning to optimise performance for large datasets.
Monitoring and alerting: Set up proactive monitoring to detect issues before they impact operations.
Cost management: Track and optimise pipeline execution to avoid unnecessary consumption.
Governance and security: Apply consistent access controls and ensure compliance with internal and external standards.
Data lineage and documentation: Maintain clear visibility of data sources, transformations, and dependencies to simplify troubleshooting and audits.
Version control and deployment: Use source control and automated deployment pipelines to promote consistency, traceability, and faster release cycles.

Seamless Data Integration

Cost-effective

Data Transformation

Hybrid Data Movement

Data Governance

Scalable and Managed Service

Orchestration and Scheduling

Monitor and Troubleshoot

Use

Azure Data Factory is suitable for use in a wide range of situations, including:

ETL Workflows

Extract data from various sources, apply transformations and load into data warehouses or other destinations.

Data Warehouse

Integrate disparate data sources into a centralised data lake or data warehouse to analyse and gain insights from data.

Live Data Stream

Ingest and process real-time data streams, enabling real-time analytics and decision-making.

Hybrid Data Migration

Migrating data from on-premises systems to the cloud without disrupting existing workflows.

Data Sync

Keep data in sync across various systems, ensuring updates in one data store are reflected in others.

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If you choose to work with us, we’ll design a strategy outlining how we can tackle your biggest pain points immediately, with a plan of action to transform you into a successful state-of-the-art data-driven business.

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We will help you design, implement, and optimise data pipelines with Azure Data Factory, making it easier to connect, transform, and deliver data across your organisation.