Making Informed Choices: Azure Data Factory or Fivetran for Data Integration

In today’s world, we live in, data has become a vital asset for organizations across all industries. While businesses understand more & more the value of data, more than ever business information gets fragmented across various silos and legacy systems. As such today, data integration tools have become essential for combining this scattered data, by efficiently extracting data from the various sources, transforming it, and loading it into your data platform. 

ADF (Azure Data Factory) and Fivetran are two popular data integration platforms that are used to move and transform data between various sources and destinations. While they both provide capabilities for extracting, loading and transforming data, they have some key differences in their approaches, features, and ideal use cases. Understanding the pros and cons of each data integration tool can help data engineers select the right tool for their needs.  

This insight will explore the distinctions between ADF and Fivetran and explore the scenarios in which each tool excels. It will help businesses make informed decisions when choosing the most suitable solution for their data integration needs. 

Understanding Azure Data Factory (ADF) and Fivetran 

Azure Data Factory logo

Azure Data Factory

Azure Data Factory is a cloud-based data integration service, developed by Microsoft, that enables the creation, orchestration, and automation of data pipelines.  

Azure Data Factory is an integration platform as a service (iPaaS), with a primary focus on ETL and ELT (Extract, Load, Transform) workflows. It provides a graphical interface to connect to various data sources, apply data transformations, and load data into data warehouses, data lakes, or analytical systems. 


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Fivetran, on the other hand, is a fully managed cloud-based data integration platform that is specialized in data replication.  

It automates the process of extracting data from various source systems, transforming it into a standardized schema (pre-built) and loading it into the destination data warehouse or analytical database.  

It offers an extensive library of 300+ prebuilt connectors to major SaaS applications, databases, APIs, and cloud data warehouses. This connector ecosystem enables Fivetran to provide integration capabilities out-of-the-box, without the need for engineering work to build custom data pipelines. 

Key Differences between ADF and Fivetran 

In the below overview, we make a deep-dive comparison of Azure Data Factory and Fivetran across a set of dimensions from architecture to reliability, scalability and business use. Here we go! 

Architecture and Integration Approach 

Azure Data Factory 

The architectural difference between Azure Data Factory and Fivetran lies in their primary purposes. ADF is a versatile data integration service with extensive data transformation capabilities and supports hybrid cloud and on-premises scenarios. It provides a visually oriented design interface that allows users to build complex data pipelines using drag-and-drop components. 


Fivetran, on the other hand, excels at data replication and synchronization, offering a simple and automated approach to quickly move data from various sources into a centralized destination for further analysis. It has a pre-built data connector approach, where users can easily integrate with a vast array of data sources without requiring manual configuration. The platform simplifies the setup process and eliminates the need for extensive coding or scripting. 

In summary, ADF as a cloud-native PaaS offering is more tightly integrated with Azure and has more extensible dev capabilities. In contrast, Fivetran is self-contained, relying less on external cloud services for its core ELT pipeline needs. As such they are complementary, and each serves their purpose in architecture. 

Data Transformation Capabilities 

Azure Data Factory 

Azure Data Factory provides a range of options from no-code drag-and-drop to fully customizable code-based data transformation capabilities as part of its integration pipelines. It’s allowing users to perform advanced data manipulations and transformations using: 

  • Data Flows: ADF provides a visually designed data transformation canvas to map fields, derive columns, aggregate data, pivot, filter, join, etc. through a no-code interface. 
  • External Tools: Data transformations can be coded via Python, Scala, or R scripts running on managed Databricks clusters integrated with ADF. 
  • Data Wrangling: The ADF data wrangling service can be used to interactively shape, cleanse, and munge datasets without writing code. 
  • Custom Logic: Data transformations can be implemented through custom code using runtimes like .NET. 


Fivetran aims to provide simpler "ETL-lite" capabilities focused on pre-built transformations suitable for loading data into warehouses. Complex data transformations typically need to be handled in the destination data warehouse or analytical system. It provides some basic built-in data transformation functionality but, it has a more limited capability compared to ADF.  

Key aspects include: 

  • Pre-built transformations: Around 30 pre-defined transformations such as filters, columns formatting and data type conversions. 
  • External Tools: For more complex transformations, Fivetran requires integrating external services like DBT, Databricks, or Spark.
  • Custom Logic: Fivetran does not allow implementing custom data transformation logic natively within its pipelines. 


Azure Data Factory 

As part of the Azure ecosystem, ADF benefits from Azure Monitor and Azure Log Analytics, enabling comprehensive monitoring, logging, and diagnostics for data pipelines. This integration offers robust management and troubleshooting capabilities. 


Fivetran provides a straightforward monitoring dashboard that displays simple pipeline success/failure status, schedule logs, basic historical sync performance and some alerting capabilities. While it simplifies monitoring, it lacks deeper observability into pipeline runs and metadata that ADF can provide through Azure's monitoring stack. 

In summary, ADF outshines Fivetran substantially when it comes to in-depth monitoring, observability, and management of data integration workflows. This aligns with the positioning of ADF being a platform where developers can build flexibility in whatever they want while Fivetran focuses on already offering an out-the-box “as a Service” working solution for integration and data synchronization (which does need less in-depth monitoring) 

Data movement 

1. Connectors & Types 

Azure Data Factory 

ADF has a vast selection of over 120+ low-code connectors. These connectors encompass 89 data sources and 33 destinations, including various applications.  

Additionally, ADF enables seamless integration with Azure services like Azure Functions, Azure Machine Learning, and Azure Data Explorer, enhancing the platform's capabilities for orchestrating data workflows with other powerful Azure components. 


Fivetran’s Software-as-a-Service has a selection of 300+ no-code connectors. These connectors cover a wide spectrum of data sources, including Databases, Applications, Files, Events & Functions. With a no-code approach, users can effortlessly set up data replication from a diverse range of sources to their desired destinations, 

Fivetran LDP has a selection of 30+ low-code connectors. This option focuses primarily on databases and SAP systems. It provides the added flexibility of hybrid and private deployment options. 

In summary, while ADF offers a substantial selection of over 120+ low-code connectors with integrations within the Azure ecosystem, it is somewhat limited when compared to Fivetran. Fivetran extends its capabilities by providing 300+ no-code connectors and an additional 30+ low-code connectors, emphasizing broader and deeper integrations across Salesforce, Cloud connections, Change Data Capture (CDC), and other diverse data sources. This makes Fivetran a more versatile option for those seeking extensive connectivity and flexibility in their data orchestration tasks. 

2. Automation 

Azure Data Factory 

ADF Automation presents a fully managed and serverless approach with a drag-and-drop user interface. It seamlessly links with various Azure services and GitHub for version control. 

However, it's worth noting that ADF Automation has some limitations when it comes to Change Data Capture (CDC), often requiring manual setup for specific use cases. 


Fivetran Automation offers a fully automated and managed data integration solution, allowing users to set up their data pipelines in just five minutes. With automatic DML and DDL updates, Fivetran ensures seamless data synchronization with destinations.  

The platform efficiently handles data written to destinations, optimizing the data transfer process. Moreover, Fivetran Automation provides multiple options for automating historical data transfers and Change Data Capture (CDC).  

In summary, both ADF and Fivetran offer robust automation capabilities, but they differ in certain critical aspects. ADF shows some limitations, especially concerning Change Data Capture (CDC), where manual setup may be necessary. On the other hand, Fivetran automation provides a fully automated data integration solution, with features like automatic DML and DDL updates, optimization of data transfers, and extensive automation options for historical data and CDC. This positions Fivetran as a more comprehensive choice for users seeking advanced automation in their data integration and orchestration processes. 

3. Reliability 

Azure Data Factory 

ADF's reliability is backed by its complete management within the Azure ecosystem, featuring its robust error detection and status tracking mechanisms. While the system largely operates autonomously, it also allows for engineering intervention and configuration decision-making. Users can customize aspects such as retry intervals, partition options, write batch timeout, column mapping, and more, enabling greater control over the data integration process. In conclusion: the platform is managed by Microsoft but anything the developer builds he/she needs to (him/herself) organize reliability for. 


Fivetran prides itself on its reliability, providing a "set it and forget it" experience to users with a 99.9% uptime. Fivetran maintains reliability through API monitoring, effectively identifying and addressing potential issues. The platform also includes schema drift management, ensuring data integrity despite changes in data sources.  

With sync consistency and idempotency, Fivetran guarantees data accuracy and avoids duplication during the replication process. Incremental data replication ensures only new or modified data is transferred, optimizing performance, and reducing resource consumption. 

4. Scalability 

Azure Data Factory 

ADF offers excellent scalability through various Azure deployment options, catering to diverse user needs. Users can choose between serverless, Azure Managed VNET, and Self-Hosted Integration options based on their requirements and preferences. 

While the platform supports CDC, its availability is subject to certain limitations based on the specific sources and targets. 


Fivetran ensures scalability with deployment options, including SaaS, Hybrid, and Private Deployment. The platform offers fast and flexible Change Data Capture (CDC) speeds, supporting continuous updates as frequently as every 1 or 5 minutes, among other options.  

Fivetran optimizes data transfer by incorporating filtering and compression techniques, resulting in low latency and minimal impact on the source systems. This scalability allows Fivetran to efficiently handle data replication for diverse user requirements while maintaining optimal performance. 

5. Flexibility 

Azure Data Factory 

ADF offers a commendable level of flexibility with a wide array of activity options and seamless integration with various Azure services. Users can leverage an extensive range of tools and services to design and customize their data workflows. 

However, it has some limitations on CDC and database replication outside a few Azure core services limiting usage and workloads. 


The SaaS offering, while limited in customization, provides users with the option to utilize an API when specific customizations are required. Additionally, the functions connector extends further flexibility by enabling custom connector options. 

On the other hand, Fivetran LDP stands out for its significant flexibility and the ability to accommodate extensive use case customization.  

6. Security 

Azure Data Factory 

ADF ensures data security with support for SSL/TLS, encryption for data at rest, Azure Key Vault integration, and integration runtime encryption. 


Fivetran prioritizes data security through secure connections (e.g., Azure Private Link), anonymization, encryption, access controls, and data deletion. The platform offers comprehensive features for protecting data integrity and privacy throughout the integration process. 

7. Engineering-Lite 

Azure Data Factory 

ADF's approach requires some data engineering knowledge for setup decisions. CDC support is available but may require custom configuration for most data sources. Users can preview data before integration, and documentation is accessible outside the user interface.  


Fivetran's approach requires minimal upfront and ongoing engineering. Users can easily design and deploy LDP infrastructure. The platform's documentation is integrated into the user interface, offering a simple step-by-step connector setup. 

8. Business Aspects 

Azure Data Factory 

ADF offers usage-based pricing for various aspects, including orchestration, data movement activity, pipeline activity, and external pipeline activity. Users have the option to choose from multiple deployment options, providing flexibility and cost optimization for their business needs. 


Fivetran operates on a consumption-based model, known as Monthly Active Rows (MAR) and is available in the Azure marketplace. Users can enjoy a free trial with no cost for initial, historical syncs, and the first 14 days of incremental syncs.

Ideal Use Cases for ADF and Fivetran 

When to Use ADF: 

Complex Data Transformation 

You can choose ADF when you require sophisticated data transformations and need to leverage Azure Databricks or Azure HDInsight for big data processing. 

Hybrid Environments 

ADF is suitable for scenarios, where data integration involves both cloud-based and on-premises data sources. 

Azure-Centric Workloads  

If your organization primarily relies on Microsoft Azure services, ADF seamlessly integrates within the Azure ecosystem, enhancing the synergy between various Azure components. 

When to Use Fivetran:

Data Replication Simplification 

When you need to replicate your data quickly and effortlessly from various sources into a data warehouse, Fivetran is an excellent choice. It is well-suited for advanced data replication needs, including SAP data replication and Change Data Capture (CDC).  

Compared to ADF, manual setup and fine-tuning are still needed for specific CDC tasks. Fivetran goes a step further and automates this process completely. 

Limited Data Transformation Requirements 

If your data transformation needs are minimal or can be handled within the destination data warehouse, Fivetran's focus on data loading and replication is advantageous. 

Rapid Deployment 

Fivetran's easy setup and automated data source connectors make it ideal for organizations looking for a fast and simple integration solution. 

Incremental load use case 

Without the right tools, synchronizing data from Salesforce into a data warehouse can be quite complex as Salesforce is a highly transactional system with data constantly being added, updated, and deleted. To accurately synchronize this data, you need a way to keep track of what data has already been copied over. 

With Azure Data Factory, you would have to handle all the complexity of incrementally syncing the Salesforce data yourself, which involves: 

  • Developing logic to query for the latest updated records since the last ETL run.
  • Persisting watermarks to track sync state. 
  • Accounting for deleted records that need to be removed from the target. 
  • Mapping Salesforce's complex object model into a simpler data structure.
  • Handling sync errors and restarts.

This can take considerable time and effort to code and maintain, especially as the Salesforce data model evolves. The ETL process must change along with it. 

With Fivetran, all that complexity is handled for you. Simply connect Fivetran to Salesforce, choose your destination data warehouse and Fivetran will automatically begin replicating the data. 

Behind the scenes, Fivetran persistently stores watermarks and change history to incrementally sync data. It understands the Salesforce object model and adapts as you add/remove objects or fields. Errors are retried and the sync state is maintained. 

Rather than building and maintaining a complex custom ETL process, Fivetran allows you to be productive right away with live Salesforce data in your data warehouse. It ensures minimal replication lag even as source data volume grows over time. The entire process is managed end-to-end by Fivetran. 

CDC use case 

Getting data out of Salesforce incrementally can be very complex due to its poor unique ID support. For many Salesforce objects like Contacts or CampaignMembers, there is no guaranteed unique field that can be used to identify new vs updated records. 

Without defined primary keys, you would have to build custom logic to match records between Salesforce and the destination, handle duplicates, and prevent data loss. This sync logic needs to closely mirror Salesforce's internal sharing and update behaviour. 

With Fivetran, all this complexity is handled for you. Fivetran employs advanced heuristics to match records between Salesforce and destination when unique IDs are unavailable. It ensures duplicates are handled properly and all record changes are accounted for. 

Instead of spending time on complex duplicate identification and record matching, Fivetran enables you to be productive on day one with your Salesforce data. It operationalizes the difficult aspects of synchronization, so you can rely on accurate data in your warehouse. 

Final thoughts & recommendations 

In summary, both Azure Data Factory (ADF) and Fivetran are robust data integration solutions, each excelling in distinct areas. ADF is well-suited for organizations with complex data transformation needs, hybrid environments, and a strong reliance on the Azure ecosystem.  

On the other hand, Fivetran shines when simplicity, rapid deployment, and automated data replication are paramount. For common SaaS applications like Salesforce, Fivetran can save huge engineering efforts compared to custom coding ETL pipelines in Azure Data Factory. 

Fivetran does not only handle Salesforce's complex CDC mechanics, but it also solves the fundamental problem of syncing data without proper unique IDs. This makes Salesforce data ingestion simple and reliable.