Data Factory Showdown: Fabric vs. Azure
In the world of data processing and data integration, Data Factory plays a crucial role in facilitating efficient workflows and enabling organizations to harness the power of their data. As far as Microsoft is concerned, there are two main options for ETL tools, Azure Data Factory(ADF) and Data Factory in Microsoft Fabric (DFiMF) . While both platforms offer robust capabilities for managing and transforming data, they exhibit distinctive features and functionalities. By understanding these disparities, organizations can make informed choices to leverage the most suitable Data Factory platform for their specific requirements. In order to understand which tool is more suitable for an organization’s needs, explanation of the tool is required.
Definition of the tools
Azure Data Factory is a powerful data integration service provided by Microsoft's Azure cloud platform. Designed to orchestrate and automate data workflows, Azure Data Factory enables seamless extraction, transformation, and loading (ETL) processes across various data sources and destinations. Furthermore, Azure Data Factory provides robust scheduling, monitoring, and auditing functionalities, enabling users to manage and track data pipelines effectively.
Microsoft Fabric is a unified platform which aims to provide a comprehensive set of data and analytics services. Microsoft Fabric includes services for data warehousing, data lakes, and machine learning. One of the tools that Microsoft Fabric is offering is Data Factory in Microsoft Fabric (DFiMF). DFiMF is a cloud based service that covers all the ETL needs of a company by connecting to a variety of sources. In general, DFiMF is a combination of ADF and Power Query Dataflows. The aforementioned technologies till now used to be separated. However, now the developers have the chance to use the combination of these tools:
Dataflows to get data, transform then and load them
Data Pipelines to control the rest of the execution by providing a way to orchestrate multiple dataflows
Exploring Shared Features
Let’s begin with the commonalities of these tool. Both of these platforms can be connected to the whole spectrum of data sources, either on-premises, cloud or SaaS. They are both cloud based platforms. Their main features are:
Data integration
Data orchestration
Scheduling
Monitoring
Error handling
Exploring Differences
On the other hand there are also some differences between these two tools.
In ADF the user could create a dataset. On the contrary, DFiMF there are no datasets, but a connection would be used to pull the data.
The data pipeline of DFiMF has more options when it comes to integration, it is also included Lakehouse, Datawarehouse and more.
In ADF the user could create a dataset. On the contrary, DFiMF there are no datasets, but a connection would be used to pull the data
Machine learning functionality that can be found within DFiMF. With this feature, the identification and application of data transformation can be done automatically.
DFiMF offers an enterprise grade solution, which mean that can be integrated very easily with a company’s existing infrastructure.
In DFiMF there is a Save As option, with which you can easily duplicate pipelines for other development purposes.
Key Factors for the final Decision
Here are some additional factors to consider that can help guide your decision-making process:
Budget: Taking your budget into account, ADF often presents a more cost-effective choice compared to DFiMF.
ADF : is a pay-as-you-go service so cost depends on the number of connections, how many times each activity in that node is run, and how much data gets moved. The cost is based on:
Data Factory Units (DFUs) : $0.00025/DFU-minute
Data Transfer : here the costs depends on the source and the destination of the data
Storage : depends on the type of storage, e.g. Standard general-purpose v2, Premium block blobs, Premium page blobs
There is a pricing calculator from Microsoft - here
DFiMF : the costs are the same as above but in addition you also have :
Costs for inactive pipelines: charged at 0.80$ per month
Costs for triggers : charged at 0.00025$/trigger/minute ( here )
Technical expertise: If you prefer a user-friendly and straightforward data integration solution, ADF may be the more suitable option. Its intuitive interface and simplified pipeline creation make it accessible to users with varying levels of technical proficiency. Conversely, DFiMF may demand a higher level of technical expertise to fully leverage its advanced capabilities and features.
Future scalability: If you anticipate future requirements for more advanced data integration functionalities, such as the need for custom connectors, intricate transformations, or specific integration scenarios, DFiMF provides a broader range of options and flexibility to accommodate these evolving needs. Microsoft is actively championing this innovative solution of DFiMF, underscoring its potential to become the prevailing industry standard.
Security and governance: Nowadays a lot of companies are very careful when it comes to sensitive data. In Fabric, protection labels are supported and furthermore, if a user marks something as highly confidential, this label would be apply to all the data spectrum in Fabric.
Both Microsoft Fabric and Azure Data Factory are powerful data integration platforms that can help businesses move, transform, and load data in the cloud. The best platform for you will depend on your specific needs and requirements. What I would personally recommend, is if you are a bigger company which is using more than one Microsoft tools, and thinking to migrate to Azure, Data Factory is the way to go. Then, you will have all your analytic tools and services under one umbrella that works in synergy and provide unified analytical experience.
