When Can Azure Data Factory Help Your Business

Used by 5,526 companies globally, Azure Data Factory (ADF) is a critical service in the Azure ecosystem. It is widely applied in domains like data engineering, data science, and business intelligence.

But what makes ADF such a popular solution? And when can your business take advantage of it?

In this article, we explore Azure Data Factory, including its workflows, pros and cons, and ideal use cases.

Azure Data Factory is Microsoft’s fully-managed, cloud-based data integration service. Its core functionality is to collect data from diverse sources and consolidate it in a single location, where it can be prepared for analytical or other workflows.

ADF was released in 2015 as a cloud alternative to SQL Server Integration Services (SSIS), Microsoft’s primary on-premises solution for data integration and Extract, Transform, Load (ETL) workflows.

Modern ADF is an efficient enterprise-grade solution that supports innovative cloud data engineering practices. Organizations across industries use it to:

• Connect to a wide range of on-premises and cloud data sources
• Move and transform varying data loads
• Prepare data for analytics and business intelligence (BI)

At the core of Azure Data Factory are Extract, Load, Transform (ELT) workflows for data integration and ingestion. Extract, Load, Transform is a cloud-first approach where data is extracted and loaded directly into a cloud data storage like Azure Data Lake or Azure Synapse Analytics before being transformed. By leveraging the massive parallel processing power (MPP) of cloud data warehouses, ELT workflows ensure fast data transformations.

In some cases, companies need to apply ETL workflows instead of ELT. Such workflows typically use the capacities of a separate server to transform data before loading it to the target system. This distinguishes ETL from ELT workflows, where data is first loaded to the target system, and the power of a cloud-based data warehouse is typically used for data transformation. While ELT workflows may be more cost-efficient and flexible, ETL workflows might be a better fit for cases where the data must be cleaned, validated, or reshaped before it can be safely loaded into the target system due to security and quality requirements.

ELT workflows in ADF are supported through features such as Mapping Data Flows for visual data transformations and the orchestration of external compute services like Azure Databricks and HDInsight.

Azure Data Factory supports several essential workflows for managing data. We’ve already touched on data ingestion and integration, so now let’s take a closer look at these and other key capabilities ADF offers.

Data ingestion and movement

With ADF, organizations can collect data from a wide range of sources, such as on-premises databases, cloud storages, hybrid storages, SaaS applications, and APIs. All the data is consolidated in a single location, such as a data warehouse, where it can be used and prepared for tasks like analytics. Data ingestion can occur in batch or streaming modes, allowing information to be available either in real time or at scheduled intervals.

Such functionality might be especially valuable for e-commerce companies that gather data from customer interactions across multiple channels. With ADF’s ingestion capabilities, your organization can collect this data in one place and analyze it to make valuable customer insights. The image below illustrates ADF’s role in a hybrid infrastructure, where data from SSIS pipelines and SQL Server is unified in Azure Managed Instance, a cloud-based data storage.

Upon ingesting data from multiple sources, Azure Data Factory ensures the secure and reliable movement of data between sources and destinations. The service can copy data across regions, between cloud services, and on-premises environments. This can help your company maintain data integrity and performance.

According to the KPMG Global Tech report, only 51% of businesses have achieved efficient levels of data interoperability by 2024. One way to guarantee that data can be effectively applied cross-functionally in different departments is to ensure its consistency through data transformation.

ADF’s ELT and ETL workflows efficiently transform data to maintain a consistent format. For such tasks, ADF uses the compute and processing power of systems like Azure Synapse Analytics, Azure Databricks, and Azure SQL Database. Azure Data Factory is an efficient tool for combining and harmonizing data from diverse systems, ensuring consistency and usability across business units.

It effectively handles structured, semi-structured, and unstructured data to support analytics, reporting, and machine learning.

Data orchestration

Azure Data Factory provides efficient functionality for managing complex data workflows. Software and data engineers can use ADF to build scalable pipelines that coordinate multiple activities, trigger actions based on schedules or events, and integrate with other Azure services to create end-to-end data processing solutions.

ADF’s data orchestration capabilities allow teams to connect and coordinate complex data workflows across hybrid and multi-cloud environments. As data volumes and systems grow, businesses need more than just isolated ETL or AI tools — they need an orchestrated framework to ensure that every process, from ingestion to transformation and analytics, runs smoothly, efficiently, and in the correct sequence.

Data monitoring and management

According to IBM’s survey on the ingenuity of generative AI, 45% of respondents identified data accuracy and bias as the biggest challenges to AI adoption. To address these issues, Azure Data Factory provides built-in monitoring capabilities accessible through a monitoring hub. The solution also efficiently integrates with Azure Monitor to ensure the quality and reliability of ingested and integrated data.

With such tools, teams can track pipeline runs, analyze performance metrics, detect failures, and configure real-time alerts. Upon identifying anomalies early, data engineers can investigate inconsistencies in data or model outputs, which leads to greater accuracy, transparency, and trust in analytical insights or AI model outputs.

ADF provides support for operational practices that allow teams to make pipelines more reliable, secure, and reusable. In particular, it can be used for:

• Creating parameterized pipelines that can be reused across multiple datasets or environments
• Implementing Azure Data Factory CI/CD for pipeline deployment using Azure DevOps or GitHub, which enables automated testing and version control
• Automating key management and credential handling with Azure Key Vault to maintain security and compliance.

With all such features, Azure Data Factory is an efficient solution for developing a comprehensive data infrastructure. The service has several significant pros, but also has some minor cons. Let’s figure it out.

ADF is a mature platform with a long track record of continuous evolution. While it doesn’t have major releases, Microsoft continuously updates the platform with new features and connectors. As of 2025, ADF is a robust, enterprise-grade data integration service that offers a comprehensive set of benefits summarized below.

Broad connectivity

Azure Data Factory supports a variety of data sources, which enables seamless integration across cloud and on-premises environments. ADF includes over 90 native connectors for other Azure services and platforms like AWS and Google Cloud. In addition, there are connectors for platforms like Salesforce, SAP, Oracle, and more. This ensures efficient Azure Data Factory integration as companies can retrieve data from a variety of sources, creating a unified data environment without budget-heavy custom development.

Scalability

Azure Data Factory efficiently handles workloads of any size. The service can process small data tasks or be used for running enterprise-scale pipelines that process terabytes of information daily. Such properties make Azure Data Factory an efficient solution for software development for startups and enterprises that treat data as their critical asset. In addition, the serverless Azure Data Factory architecture automatically scales resources based on demand, which ensures high performance without manual infrastructure management.

Flexible data transformation

As previously mentioned, Azure Data Factory supports both ETL and ELT approaches to data transformation. While the ELT approach is more cloud-native, ETL workflows may be more fitting for organizations that work under strict security regulations. In addition, the solution supports both code-free transformations with Mapping Data Flows and code-based transformations based on Azure Databricks, HDInsight, or Synapse analytics. This flexibility allows you to balance speed, scalability, and technical complexity by enabling both rapid, code-free transformations and advanced, code-based processing for large-scale analytics.

Cost-effectiveness

Azure Data Factory offers a flexible pricing model, as organizations are charged on a pay-as-you-go basis. As a result, businesses pay only for what they use. The table below illustrates the basics of the ADF’s pricing model.

The pricing model of Azure Data Factory is cost-effective and helps minimize upfront cloud investment. However, it can be somewhat complex, as total costs depend on several factors that need to be carefully considered during budgeting, which include:

The number of pipeline activities

• Data movement volumes
• Integration runtime types
• Data flow execution time
• Region

We suggest using the Azure Pricing Calculator for precise estimations of ADF costs.

Native integration with the Microsoft ecosystem

ADF natively integrates with the wider ecosystem of Microsoft-based services, including Azure Synapse Analytics, Power BI, and Azure Machine Learning. This seamless connectivity simplifies workflows and ensures greater consistency across processes. A great example of an organization benefiting from a complete Azure-based ecosystem is Heathrow Airport in London. With a unified Azure environment, the airport maintains consistent, well-connected data systems. This allows Heathrow Airport to leverage advanced analytics and AI to improve operational efficiency and enhance the customer experience.

Azure Data Factory has several downsides that are worth mentioning. Notably, most of them can be mitigated with the right approach.

Steep learning curve

ADF provides a visual interface for creating pipelines. However, understanding its full potential may be hard for specialists who are new to the platform. Learning to use the platform’s dynamic content, parameterization, and expressions can take time.

Certain ways to solve this problem include extensive training with resources like Microsoft Learn and the use of prebuilt templates and consistent parameterization practices. This helps new users learn faster and reduce errors. Over time, mentorship and version-controlled experimentation with Azure DevOps or GitHub allows teams to confidently master the platform’s advanced functionality.

Tricky debugging

The complexity of multi-step data flows can make troubleshooting pipelines in Azure Data Factory a challenging task. Even with ADF’s built-in monitoring and logging tools, pinpointing bottlenecks often takes time. This can cause delays or disruptions in critical analytical workflows. It is an especially serious issue for industries that depend on real-time insights, such as the financial sector.

One way to solve this problem is by breaking complex pipelines into smaller, modular components that are less challenging to monitor. Additionally, we suggest that teams test pipelines in a controlled environment with version control in place.

Limited on-premises focus

Azure Data Factory is an efficient solution for cloud and hybrid environments, but its capabilities for purely on-premises data orchestration are limited. For instance, ADF doesn’t support native, on-premises scheduling for data integration. Its pipelines are triggered via the cloud, and there’s no built-in agent for fully local scheduling independent of Azure. Therefore, Azure Data Factory might not be the optimum choice for organizations that rely heavily on on-premises data systems.

A possible solution for such companies involves configuring a self-hosted integration runtime. This enables them to securely connect, move, and transform on-premises data within ADF pipelines while maintaining control over local resources and network access. In addition, companies can combine ADF with other tools oriented towards on-premises deployments to achieve full coverage.

Azure Data Factory shines when organizations need to integrate, orchestrate, and manage data across multiple systems efficiently. Let’s explore the most common business scenarios where such ADF’s capabilities deliver clear value.

Enterprise data migration to the cloud

With its powerful data ingestion and integration capabilities, Azure Data Factory offers an effective solution for enterprises that need to securely and efficiently migrate large volumes of data from on-premises or legacy systems to the cloud. ADF works well even with environments with diverse infrastructures spanning on-premises, cloud, and hybrid deployments. The service can use its variety of connectors and integration runtimes to integrate with all data sources, ensuring unified data movement without disrupting ongoing operations.

Post-merger unification of data infrastructure

ADF is an efficient solution for unifying data assets of different companies after mergers and acquisitions. The service excels at integrating disparate systems, databases, and applications from different organizations into a single and consistent environment.

For instance, Leobit team helped a global e-commerce company build an efficient workflow for data orchestration and business intelligence using ADF’s integration and ingestion capabilities. Later, when the company was acquired by a larger enterprise, this workflow became a solid foundation for seamlessly unifying data assets between both organizations.

Advanced analytics and AI development

ADF serves as a foundation for AI and machine learning by helping to prepare clean, reliable, and well-labeled datasets. It also seamlessly integrates with Azure AI Services, such as Azure Machine Learning. Azure Data Factory effectively automates the collection and preparation of data, which makes it a valuable tool for designing data flows that feed and support ML models.

Unified reporting and business intelligence

Azure Data Factory allows organizations to eliminate data silos across departments and platforms. By consolidating multiple sources, such as marketing systems and CRMs, into a unified, well-structured data model, it supports consistent and comprehensive analytics using tools such as Power BI and Azure Synapse Analytics. In fact, it makes ADF a powerful foundation for implementing modern data-driven architectures like data fabric, an approach that connects all data across different platforms and locations, making it easier to access, manage, and analyze from one place. ADF can also become a fundamental tool in establishing data mesh, a data architecture where each team within the organization handles and shares its own data instead of having one central system.

Compliance and regulatory data management

ADF is an effective solution for industries subject to strict regulations, such as healthcare, where failure to protect patient data can result in penalties of up to $1,500,000 under HIPAA. The service provides efficient monitoring, logging, and governance capabilities that help data teams ensure that data movement, lineage, and access are fully traceable and secure.

All data infrastructures are unique, shaped by varying compliance requirements, data sources, and workflows. To maximize business value from the service and build a tailored Azure Data Factory architecture, it may be necessary to use service’s advanced configurations and custom pipelines. This requires specialists with strong expertise in Microsoft technologies and Azure Data Factory best practices. If you lack such experts in-house, you can outsource ADF configuration and pipeline deployment to a qualified dedicated team.

Leobit is a company with Azure technologies at the heart of our services and is always ready to assist. We are a Microsoft Solutions Partner for Digital and App Innovation, with extensive Azure development experience gained from over 45 successful projects. In addition, our team possesses significant AI expertise, including configuring AI-powered analytics and insights, integrating AI and machine learning, and developing custom large language models (LLMs). By combining this technical depth with a client-focused approach, Leobit ensures your Azure Data Factory setup is efficient and fully aligned with your business goals.

Azure Data Factory is a key tool for building an effective data architecture that fosters a data-driven culture within organizations. While it supports multiple workflows, its primary function is to gather data from diverse sources, load it into storage or a data warehouse, and prepare it for subsequent workflows such as data analytics.

ADF can be an excellent solution for businesses looking to:

• Migrate enterprise data to the cloud
• Ensure unified reporting and business intelligence
• Consolidate data infrastructure after a merger
• Maintain compliance and efficient data monitoring
• Effectively manage data for advanced analytics and AI initiatives

The main challenge is that configuring Azure Data Factory can be complex, requiring strong experience with the Azure stack. Contact Leobit, and we will provide the expertise you need to fully leverage the power of Azure Data Factory.