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Azure Data Factory
Create, orchestrate and schedule data workflows at scale
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Azure Data Factory
Azure Data Factory is a cloud-based data integration service from Microsoft that enables users to create, schedule and orchestrate data workflows at scale.
Build scalable ETL and data pipelines
Design data-driven workflows that automate extract, transform and load (ETL) processes with minimal code and high performance.
Connect and transform data across diverse sources
Azure Data Factory integrates with a wide range of on-premises and cloud data stores, allowing you to move and reshape data between environments with consistency and control.
Enable data-driven insights and analytics
By organising and integrating data across your sources, Azure Data Factory helps ensure your analytics, dashboards and reporting tools are fed with accurate, up-to-date information.
Our Clients
Businesses that have trusted us to implement Microsoft Azure solutions successfully
Key features of Azure Data Factory
Azure Data Factory offers the tools organisations need to manage cloud-based data movement and transformation across distributed environments.
Visual data pipeline designer
A drag-and-drop interface that allows users to design data workflows without writing code. Ideal for managing complex integrations through a visual approach.
Extensive data connectors
Connects with over 100 native connectors, including Azure services (Blob Storage, SQL Database, Synapse Analytics), on-premises systems and third-party platforms such as Amazon S3, Salesforce and Oracle.
Code-free and code-centric authoring
Supports both low-code UI-based pipeline creation and full control via JSON and Azure Resource Manager templates for advanced customisation and automation.
Data movement and transformation at scale
Automates extract, transform and load (ETL/ELT) processes using built-in data flow components or integration with external compute services like Azure HDInsight, Databricks and SQL Server Integration Services (SSIS).
Hybrid data integration
Access on-premises data securely with the self-hosted integration runtime. Designed to support hybrid environments without exposing internal systems externally.
Monitoring and management
Built-in monitoring and logging features offer real-time visibility into pipeline executions, with detailed metrics and alerts to support operational efficiency.
Scalability and resilience
Designed for high availability and scalability, with support for parallel processing, fault tolerance and retry policies to ensure reliable execution of mission-critical data workflows.
Not sure where to start with Azure Data Factory? Book your free consultation
If you're evaluating Azure Data Factory or trying to make sense of how it fits into your current data ecosystem, let's talk. Claria offers a free consultation focused on your architecture, data flows and delivery goals without assumptions or pressure.
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Benefits of Azure Data Factory
Accelerated data integration delivery
Design and deploy data pipelines using a visual interface and prebuilt connectors, helping reduce development time and making data available more quickly.
Improved operational efficiency
Automate routine data movement and transformation tasks, allowing teams to redirect efforts toward analysis and strategic initiatives.
Consistent hybrid connectivity
Integrate data from cloud and on-premises sources securely, supporting unified processing across different environments.
Scalable and flexible architecture
Easily scale from small data flows to complex enterprise pipelines, adapting to growing data volumes and business requirements.
Resilient and dependable workflows
Built-in monitoring, logging and error handling features ensure reliable execution of critical data operations.
Enterprise-level security and governance
Supports secure data transfers, access control and compliance standards to protect sensitive information and meet regulatory needs.
How we help with Azure Data Factory
Practical support to plan, implement and maintain reliable data workflows with Azure. As a Microsoft Azure Partner, Claria helps organisations make Azure Data Factory work within the context of their architecture, operations and data goals.
Our Azure Data Factory services
Data pipeline design and architecture
We design data pipelines that reflect your operational priorities, balancing performance, scalability and cost efficiency across systems and teams.
Monitoring, support and managed services
We provide day-to-day operational support, incident response and service monitoring to keep your data processes consistent and dependable over time.
Implementation and integration
We configure and deploy Azure Data Factory to connect your cloud and on-premises sources, ensuring automated, consistent data movement aligned with your platform needs.
Azure Data Factory training
We deliver Azure Data Factory training sessions tailored to your teams, covering everything from pipeline authoring to monitoring and deployment practices.
Migration from legacy ETL platforms
We help you transition away from outdated ETL tools, reducing complexity and aligning your integration stack with modern Azure services.
Team augmentation
Need additional expertise during a project or rollout? Our data integration specialists can work alongside your team to deliver and transfer knowledge.
Pipeline optimisation and tuning
Our expert team reviews and refines your existing pipelines to improve performance, reduce processing delays and control compute costs.
Data governance & compliance
We implement governance frameworks, data lineage tracking and access controls within Azure Data Factory to meet security, compliance and auditability requirements.
Azure Data Factory Pricing and Costs
Understand what Azure Data Factory will cost based on how you use it
Azure Data Factory uses a pay-as-you-go pricing model, where costs are based on actual usage rather than fixed licenses or user counts. With costs applied per execution of each pipeline, as well as for compute duration, designing and developing efficient pipelines is the key to making the most of Data Factory.
At Claria, we help you understand how Azure Data Factory’s pricing structure aligns with your data workflows, so you can estimate costs with accuracy and plan for scale.
See Azure Data Factory prices
Key factors affecting Azure Data Factory pricing
Factors impacting the overall cost of an Azure Data Factory solution include:
Pipeline orchestration and execution
Charges are based on the number of pipeline activities run and their type, with data movement, data transformation and control activities each incurring different charges.
Data movement and volume
Costs depend on the volume of data moved between sources and destinations, especially across regions or between on-premises and cloud environments.
Data flow usage
Mapping data flows (for transformations) are billed based on execution time and compute usage, with pricing varying according to the size and duration of data flow clusters.
Integration runtime type
Different integrations runtimes incur different costs based on compute resources, execution time and region.
Frequency and concurrency of pipeline runs
Higher frequency or simultaneous pipeline executions may increase consumption and impact overall costs.
How Claria helps with Azure Data Factory cost planning
As Microsoft Azure partners, we work with your data and infrastructure teams to:
Estimate expected activity usage across development and production
Model cost scenarios based on pipeline frequency and data volume
Optimise integration runtime configurations to avoid overprovisioning
Review existing implementations for cost efficiency execution (Service Bus)
Provide visibility into usage patterns for ongoing budgeting and forecasting
How Azure Data Factory works
Azure Data Factory enables data integration and transformation through a visual, data-driven pipeline model designed for building scalable, automated workflows. This visual and flexible solution offers a relatively simple approach to the creation of enterprise-grade data integration workflows while negating heavy code dependencies.
Here are the essential steps required to get going with Azure Data Factory:
1. Start with a Pipeline
The core component behind every Azure Data Factory solution is a pipeline, which is a logical grouping of activities that define how data is moved and transformed from source to destination.
2. Define Activities and Flow
Pipelines contain activities such as data movement (copying data), transformation (using data flows), or control logic (like looping or conditional branching). Once defined, these activities are connected to define the flow of your data process.
3. Connect to Data Sources
Azure Data Factory includes a wide range of built-in connectors to access cloud and on-premises data sources such as Azure SQL Database, Amazon S3, Oracle, Salesforce, SAP and more.
4. Use Integration Runtime
The Integration Runtime (IR) is the compute infrastructure used to perform actions. Choose from Azure-hosted, self-hosted, or the Azure SSIS IR, depending on where the data lives and the type of integration required.
5. Schedule and Trigger Pipelines
Pipelines can be triggered on a schedule, in response to events, or on-demand, enabling you to automate your data workflows based on business needs or system changes.
6. Transform Data at Scale
Use Mapping Data Flows or external services like Azure Databricks to perform transformations such as cleaning, joining, aggregating, or reshaping data as needed.
7. Monitor and Manage
Built-in monitoring tools allow you to track pipeline runs, review performance metrics, identify failures and take corrective action through alerts or retries.
When to use Azure Data Factory
With its vast data transformation capabilities, capacity to accommodate a broad range of sources and variety of trigger mechanisms, Azure Data Factory provides a reliable solution for various needs.
In short, Azure Data Factory is a good fit when consistency, flexibility and operational control are priorities in your data integration strategy.
Get in touchConsider Azure Data Factory if you need to:
Ingest and transform data from multiple sources
Move and shape data from cloud and on-premises systems such as SQL Server, SAP, Salesforce and Azure Blob Storage into a centralised destination for analytics or reporting
Automate data workflows at scale
Orchestrate complex ETL processes that run on a schedule or in response to events, eliminating the need for manual intervention and reducing operational overhead
Migrate or modernise legacy ETL systems
Replace traditional tools like SSIS or Informatica with a modern, cloud-native integration service that supports hybrid deployments and cloud scalability
Build hybrid data integration solutions
Use the self-hosted integration runtime to securely access internal data sources without needing to expose them to external networks.
Enable real-time and batch data processing
Support both real-time ingestion scenarios and scheduled batch processing to keep data fresh and aligned with business intelligence needs
Why choose Claria?
Your trusted Azure Data Factory partner
As a certified Microsoft Azure Partner, Claria helps organisations use Azure Data Factory as a reliable part of their data architecture.
We understand that success with Azure Data Factory isn’t about spinning up pipelines quickly. It’s about making sure they run in the right context, with the right control, and for the right reasons.
Here’s how we make the difference:
We start with architecture, not activities
Before building pipelines, we work with you to understand your data flow landscape, storage models and processing demands. This ensures Azure Data Factory fits into your wider data strategy.
We help you control costs before they grow
We focus on the things that quietly drive up cost, like inefficient activity runs, poorly configured runtimes or unnecessary data flows, and help you fix them early.
We design for people, not just services
Our solutions aren’t just scalable, they’re manageable. We align pipeline logic with how your teams will monitor, support and evolve the platform over time.
We translate messy data realities into clear pipelines
We help simplify data coming from fragmented sources and legacy platforms, so what reaches your BI or analytics tools is usable, reliable and on time.
We build for ownership, not dependency
We stay close during design and implementation but our goal is to hand off a system your teams can maintain and adapt without external reliance.
Azure Data Factory gives you the tools. Claria helps you apply them with discipline, clarity and confidence.
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Azure Data Factory FAQs
Azure Data Factory is a cloud-based data integration service that allows you to create, schedule and manage data pipelines to move and transform data from various sources to destinations, including cloud and on-premises systems. It's most typically used for ETL/ELT processes, data warehousing and analytics preparation.
Yes. Azure Data Factory can connect to on-premises data sources securely using a self-hosted integration runtime. This acts as a bridge between your local environment and the cloud, negating the need for you to expose data publicly over the internet.
Azure Data Factory pricing is based on usage, with factors such pipeline orchestration, data movement, data flow execution and the type of integration runtime used all contributing to the pricing model. Costs also depend on factors like volume of data processed, execution time and frequency of activity.
Azure Data Factory is primarily designed for batch and scheduled processing of large volumes of data, but it can handle near real-time scenarios when triggered by events or combined with other Azure tools such as Event Grid or Azure Functions. For strict real-time streaming, Azure Stream Analytics may be a better fit.
Whereas SQL Server Integration Services (SSIS) is a traditional on-premises ETL tool, Azure Data Factory is a cloud-native, scalable,and serverless data integration service. Consequently, Data Factory supports broader integration scenarios (cloud/SaaS/on-prem), hybrid connectivity, while offering easier maintenance with a low-code approach.
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