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 data mapping and the ability to systematically derive data are nice features. It worked really well for the solution we had. It is visual, and it did the transformation as we wanted."
"The flexibility that Azure Data Factory offers is great."
"The function of the solution is great."
"On the tool itself, we've never experienced any bugs or glitches. There haven't been crashes. Stability has been good."
"The solution is okay."
"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."
"The most valuable feature is the copy activity."
"The overall performance is quite good."
"The most valuable features of Microsoft Azure Synapse Analytics are how easy and quick it is to set up the linked services."
"We've had a good experience with technical support in general."
"The MPP (Massively Parallel Processing) architecture helps to make things a lot faster."
"Data can be stored any way you want in the data warehouse."
"It's feature-rich. It has a wide range of features."
"It is a fantastic product; we are satisfied with its features and performance."
"The setup is pretty simple."
"It's quite quick for querying, even with large datasets, and it's scalable. It's also flexible to use, so it's easy to update and get data quickly without wasting time."
"Some known bugs and issues with Azure Data Factory could be rectified."
"The pricing model should be more transparent and available online."
"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."
"Azure Data Factory can improve the transformation features. You have to do a lot of transformation activities. This is something that is just not fully covered. Additionally, the integration could improve for other tools, such as Azure Data Catalog."
"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 Microsoft documentation is too complicated."
"This solution is currently only useful for basic data movement and file extractions, which we would like to see developed to handle more complex data transformations."
"It's pay as you go, so you never know what your bill is going to be beforehand, and that's scary for customers. If you have someone who makes a mistake and the program's a loop that is running all night, you could receive a very expensive bill."
"The initial setup process needs improvement. When you're moving to the cloud it takes a bit of time. It would be great if they could implement something that would make it faster."
"The solution does not support oriented scaling in the synapse."
"Unfortunately, we have had some issues with the dashboard reporting. Sometimes, the data for specific periods would just appear blank on the dashboard. To investigate this, we worked with a Microsoft incident agent and it turned out to be a result of bugs in the platform when dealing with specific types of queries in Azure Data Factory."
"I am pretty sure that there are areas that need improvement but I just can think of them off the top of my head."
"The platform is not flexible, and the graphical user interface needs to be improved because the interface makes it hard for the end user to use it."
"This is a young product in transition to the cloud and it needs more work before it is both settled as a product and competitive in the market."
"I would like my team to be able to build pipelines that integrate with the Azure Data Factory."
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!!