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Azure Data Factory pros and cons

Vendor: Microsoft
4.0 out of 5
Badge Ranked 1
1,342 followers
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Pros & Cons summary

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Prominent pros & cons

PROS

Azure Data Factory enables seamless data integrations and transformations with over 100 built-in connectors and the capability to scale efficiently as business needs grow.
Copy Activity is highly regarded for its ability to transfer data across environments, supporting large-scale data movement.
Flexibility and integrability are highlighted as core strengths, allowing Azure Data Factory to work effectively with other Azure components.
The platform offers a powerful yet user-friendly experience for creating ETL pipelines, enabling quick and efficient data workflows.
Azure Data Factory is praised for its scalability, reliability, and ease of use, making it a preferred choice for Microsoft-centric organizations.

CONS

Azure Data Factory requires better integration with Azure machine learning and other providers, such as Oracle BI.
The pricing model of Azure Data Factory is complex and difficult to understand, posing challenges for smaller businesses.
There is a need for more third-party extensions, plugins, and connectors, especially for transformation using data flow activity.
The initial setup and configuration process of Azure Data Factory is not straightforward, requiring improvement.
Azure Data Factory struggles with performance during heavy data processing and lacks features for real-time data replication.
 

Azure Data Factory Pros review quotes

GM
Dec 13, 2022
The trigger scheduling options are decently robust.
Emad Afaq Khan - PeerSpot reviewer
Oct 11, 2022
Azure Data Factory became more user-friendly when data-flows were introduced.
AS
Dec 22, 2022
The data factory agent is quite good and programming or defining the value of jobs, processes, and activities is easy.
Learn what your peers think about Azure Data Factory. Get advice and tips from experienced pros sharing their opinions. Updated: December 2024.
824,053 professionals have used our research since 2012.
Mano Senaratne - PeerSpot reviewer
Dec 1, 2022
The feature I found most helpful in Azure Data Factory is the pipeline feature, including being able to connect to different sources. Azure Data Factory also has built-in security, which is another valuable feature.
KR
Mar 6, 2024
I like its integration with SQL pools, its ability to work with Databricks, its pipelines, and the serverless architecture are the most effective features.
RD
Aug 11, 2021
It is very modular. It works well. We've used Data Factory and then made calls to libraries outside of Data Factory to do things that it wasn't optimized to do, and it worked really well. It is obviously proprietary in regards to Microsoft created it, but it is pretty easy and direct to bring in outside capabilities into Data Factory.
reviewer2378058 - PeerSpot reviewer
Mar 13, 2024
The scalability of the product is impressive.
Brian Sullivan - PeerSpot reviewer
Jul 28, 2022
It's cloud-based, allowing multiple users to easily access the solution from the office or remote locations. I like that we can set up the security protocols for IP addresses, like allow lists. It's a pretty user-friendly product as well. The interface and build environment where you create pipelines are easy to use. It's straightforward to manage the digital transformation pipelines we build.
Dan_McCormick - PeerSpot reviewer
Jun 3, 2022
The most valuable feature of Azure Data Factory is that it has a good combination of flexibility, fine-tuning, automation, and good monitoring.
Thulani David Mngadi - PeerSpot reviewer
Mar 18, 2024
The workflow automation features in GitLab, particularly its low code/no code approach, are highly beneficial for accelerating development speed. This feature allows for quick creation of pipelines and offers customization options for integration needs, making it versatile for various use cases. GitLab supports a wide range of connectors, catering to a majority of integration needs. Azure Data Factory's virtual enterprise and monitoring capabilities, the visual interface of GitLab makes it user-friendly and easy to teach, facilitating adoption within teams. While the monitoring capabilities are sufficient out of the box, they may not be as comprehensive as dedicated enterprise monitoring tools. GitLab's monitoring features are manageable for production use, with the option to integrate log analytics or create custom dashboards if needed. The data flow feature in Azure Data Factory within GitLab is valuable for data transformation tasks, especially for those who may not have expertise in writing complex code. It simplifies the process of data manipulation and is particularly useful for individuals unfamiliar with Spark coding. While there could be improvements for more flexibility, overall, the data flow feature effectively accomplishes its purpose within GitLab's ecosystem.
 

Azure Data Factory Cons review quotes

GM
Dec 13, 2022
There is no built-in pipeline exit activity when encountering an error.
Emad Afaq Khan - PeerSpot reviewer
Oct 11, 2022
Azure Data Factory uses many resources and has issues with parallel workflows.
AS
Dec 22, 2022
The pricing model should be more transparent and available online.
Learn what your peers think about Azure Data Factory. Get advice and tips from experienced pros sharing their opinions. Updated: December 2024.
824,053 professionals have used our research since 2012.
Mano Senaratne - PeerSpot reviewer
Dec 1, 2022
Areas for improvement in Azure Data Factory include connectivity and integration. When you use integration runtime, whenever there's a failure, the backup process in Azure Data Factory takes time, so this is another area for improvement.
KR
Mar 6, 2024
There is room for improvement primarily in its streaming capabilities. For structured streaming and machine learning model implementation within an ETL process, it lags behind tools like Informatica.
RD
Aug 11, 2021
There is always room to improve. There should be good examples of use that, of course, customers aren't always willing to share. It is Catch-22. It would help the user base if everybody had really good examples of deployments that worked, but when you ask people to put out their good deployments, which also includes me, you usually got, "No, I'm not going to do that." They don't have enough good examples. Microsoft probably just needs to pay one of their partners to build 20 or 30 examples of functional Data Factories and then share them as a user base.
reviewer2378058 - PeerSpot reviewer
Mar 13, 2024
The product's technical support has certain shortcomings, making it an area where improvements are required.
Brian Sullivan - PeerSpot reviewer
Jul 28, 2022
Data Factory has so many features that it can be a little difficult or confusing to find some settings and configurations. I'm sure there's a way to make it a little easier to navigate.
Dan_McCormick - PeerSpot reviewer
Jun 3, 2022
They require more detailed error reporting, data normalization tools, easier connectivity to other services, more data services, and greater compatibility with other commonly used schemas.
Thulani David Mngadi - PeerSpot reviewer
Mar 18, 2024
Azure Data Factory could benefit from improvements in its monitoring capabilities to provide a more robust feature set. Enhancing the ease of deployment to higher environments within Azure DevOps would be beneficial, as the current process often requires extensive scripting and pipeline development. It is also known for the flexibility of the data flow feature, particularly in supporting more dynamic data-driven architectures. These enhancements would contribute to a more seamless and efficient workflow within GitLab.