We performed a comparison between Azure Data Factory and Microsoft Azure Synapse Analytics based on our users’ reviews in four categories. After reading all of the collected data, you can find our conclusion below.
Comparison Results: Both of these solutions are very dynamic, robust, stable, and very flexible. As they are both part of the Microsoft Azure ecosystem, they are both very popular and highly regarded. Many of our users feel Azure Data Factory is an easier solution to understand and get started with out of the box. Microsoft Azure Synapse Analytics is more diverse and works better with a varied amount of different areas and industries.
"The most valuable feature of this solution would be ease of use."
"Most of our customers are Microsoft shops and prefer Azure Data Factory because they have good licensing options and a trust factor with Microsoft."
"The solution can scale very easily."
"The security of the agent that is installed on-premises is very good."
"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."
"We have found the bulk load feature very valuable."
"UI is easy to navigate and I can retrieve VTL code without knowing in-depth coding languages."
"The flexibility that Azure Data Factory offers is great."
"They are available on the Cloud, and the platform is very intuitive."
"The MPP (Massively Parallel Processing) architecture helps to make things a lot faster."
"It's feature-rich. It has a wide range of features."
"We have found that it is easy to develop and to do the analytics in the modules of data."
"The most valuable features of Microsoft Azure Synapse Analytics are its serverless flexibility and complete power have allowed me to explore various different use cases. While I am not an expert in the product, my experience in programming in Databricks has shown me that Microsoft's investments in Synapse could potentially lead to it becoming a complete replacement for Databricks in the future."
"The features most valuable are the simplicity, how easy it is to create a dashboard from different information systems."
"The most valuable feature of Microsoft Azure Synapse Analytics is its integration with the new legacy systems. Whatever application we want to integrate, we receive the reports based on the objects. The solution is easy to purchase from the cloud."
"Scaling this solution is easy and the uptime is okay."
"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."
"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."
"It can improve from the perspective of active logging. It can provide active logging information."
"My only problem is the seamless connectivity with various other databases, for example, SAP."
"Integration of data lineage would be a nice feature in terms of DevOps integration. It would make implementation for a company much easier. I'm not sure if that's already available or not. However, that would be a great feature to add if it isn't already there."
"The product's technical support has certain shortcomings, making it an area where improvements are required."
"User-friendliness and user effectiveness are unquestionably important, and it may be a good option here to improve the user experience. However, I believe that more and more sophisticated monitoring would be beneficial."
"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."
"The filing can be improved."
"Indicating what areas need improvement in this solution is a difficult question because the organizations that I am working for are really new in this area. However, an even better more simple interface, or perhaps an extension of a connector app store solution, would be helpful."
"There may be some challenges in terms of connecting with Virtual Networks (VNETs) to Microsoft Azure Synapse Analytics."
"Integration with other vendors has limitations and could be improved."
"It's a complicated product."
"An area for improvement in Microsoft Azure Synapse Analytics is its user interface. You can use it for analytical purposes, but its platform should be a little bit more user-friendly. Another small point for improvement in Microsoft Azure Synapse Analytics is its stability. It's good currently, but it could still be improved. Microsoft is combining different tools and technologies into one solution, so in the future, I'm expecting to see even more improvement in Microsoft Azure Synapse Analytics. An additional feature I'd like to see in the next version of Microsoft Azure Synapse Analytics is the drag-and-drop feature. If you're doing some integrations where you can write Scala or you have SPARK programming or SQL, or you're combining different programming, the process should be seamless, and you should be able to drag and drop in Microsoft Azure Synapse Analytics. When doing reporting in the solution, you should also be able to drag and drop. There should be connectors available and a drag-and-drop feature available in the user interface of Microsoft Azure Synapse Analytics, so you won't have to worry about how all processes would work together. You need to be able to drag and drop even from the backend, and having this feature will make the solution more user-friendly."
"The only concern for us is the cost part. When it comes to the implementation and the support and maintenance, we see high-cost implications."
"It could be beneficial to focus on integration with various data sources and similar enhancements."
More Microsoft Azure Synapse Analytics Pricing and Cost Advice →
Azure Data Factory is ranked 3rd in Cloud Data Warehouse with 81 reviews while Microsoft Azure Synapse Analytics is ranked 2nd in Cloud Data Warehouse with 86 reviews. Azure Data Factory is rated 8.0, while Microsoft Azure Synapse Analytics is rated 7.8. The top reviewer of Azure Data Factory writes "The data factory agent is quite good but pricing needs to be more transparent". On the other hand, the top reviewer of Microsoft Azure Synapse Analytics writes "No competitors provide the entire solution to one place ". Azure Data Factory is most compared with Informatica PowerCenter, Informatica Cloud Data Integration, Alteryx Designer, Snowflake and Oracle Data Integrator (ODI), whereas Microsoft Azure Synapse Analytics is most compared with SAP BW4HANA, Snowflake, Oracle Autonomous Data Warehouse, Teradata and Amazon Redshift. See our Azure Data Factory vs. Microsoft Azure Synapse Analytics report.
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I know you're looking for someone who's done research for you but realize that's actually something people get paid to do.
That said, what you're asking about is a mix of quite different tools when you throw KNIME in the mix. I don't know that tool but sounds like its for specific purpose and it's not an Azure tool. Realize there's endless ETL tools out there. I've used about 1/2 dozen in my career. I currently use both ADF and SSIS. I only use ADF when I have to as it's overly complicated to do version management and deal with ARM templates and is very very slow in comparison to SSIS. ADF can however be a good orchestrator for running SSIS - there's an Azure/PaaS version of SSIS called SSIS-IR that can run from ADF. Synapse Analytics pipelines which is actually ADF technology but stripped down. And now there's Fabric Data Factory which is again ADF but even more stripped down. Fabric is also bleeding edge.
ADF has been around for long time now. Anything Azure is cloud based and integrates with Azure services. KNIME is not that. I advise first on understanding fundamental requirements such as, what are the skill levels of your staff with ETL? Are you an Azure shop? What kind of data volumes are you talking about? What sources do you need to connect to (that's a biggy because not all tools talk to all sources!) What are you trying to do - build a datamart or EDW or just copy some data from a source or ? Do you use PowerBI? These will help drive what kind of tool you're looking for. If you want SAAS like as possible tool due to minimal requirements, low data volumes and low staff expertise and starting from scratch, I'd give Fabric a try especially if you want low tech and already into the Power platform. Hope that helps
I believe Synapse is not an ETL tool. ADF is one optional ETL tool for a Synapse Data warehouse.. What Are the Top ETL Tools for Azure Data Warehouse? | Integrate.io
I'd like to step back and pose a bigger option. You see, ETL means making a copy of data you have already. Have you considered a data fabric or mesh, where the data is used where it lies now? Consider this if your data is already used by some systems, but you need to do a more comprehensive analysis of it.
I always want to reduce the replication of databases. The concept of build yet another database to "replace" all the others rarely works out that way. I'd rather beef up the origination system, or use a replica than build a huge portfolio of ETL programs and an army of ops, data governance, and system support to keep them in sync.
Finally, if you really need an ETL tool, i.e. copies of all that data... look for existing talent in your staff. Otherwise, expect to hire some people experienced with the new tool that can advise on design and development and mentor existing staff.
A couple of questions before starting the feature comparison: i. Are you fine with an open-source solution? ii. Any specific reason you have listed ADF? iii. Who will be using these tools and how much learning curve is involved within the team? iv. What kind of data you are dealing with? v. Is data privacy an important factor? vi. Are you looking for only a cloud-based solution or open to a hybrid solution also? vii. What is the maturity level of the team when it comes to working on the cloud ........ These are just a few of the many questions basis which we do self-assessment or measure our preparedness. Let me know if you need more insights. Happy to help!!