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.
"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."
"It makes it easy to collect data from different sources."
"An excellent tool for pipeline orchestration."
"The best part of this product is the extraction, transformation, and load."
"The data copy template is a valuable feature."
"The most valuable features are data transformations."
"This solution will allow the organisation to improve its existing data offerings over time by adding predictive analytics, data sharing via APIs and other enhancements readily."
"Data Factory's best feature is the ease of setting up pipelines for data and cloud integrations."
"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."
"We find the serverless tool to be the most valuable feature ."
"Azure Synapse combines the strengths of SQL technologies for effective enterprise data management."
"Synapse Analytics' best features are notebooks, pipelines, and monitoring."
"It is a fantastic product; we are satisfied with its features and performance."
"I like SQL post, which is for storage and distributed computing. Another good feature is the copy activity."
"The solution has been working well overall."
"Scaling this solution is easy and the uptime is okay."
"The need to work more on developing out-of-the-box connectors for other products like Oracle, AWS, and others."
"Data Factory would be improved if it were a little more configuration-oriented and not so code-oriented and if it had more automated features."
"There is no built-in function for automatically adding notifications concerning the progress or outline of a pipeline run."
"Sometimes I need to do some coding, and I'd like to avoid that. I'd like no-code integrations."
"There are limitations when processing more than one GD file."
"A room for improvement in Azure Data Factory is its speed. Parallelization also needs improvement."
"The performance could be better. It would be better if Azure Data Factory could handle a higher load. I have heard that it can get overloaded, and it can't handle it."
"Currently, our company requires a monitoring tool, and that isn't available in Azure Data Factory."
"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 solution does not support oriented scaling in the synapse."
"With respect to what needs to be improved, concurrent connectivity has some limitations."
"Synapse Analytics' performance slows down if you don't get your distribution right because it gets queued and goes into a single node."
"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."
"I am very sure that there are areas in need of improvement, but I can't recall what they are off the top of my head."
"The only issue that we have run into with the solutions performance is with regards to concurrency."
"The security performance and cost are the two things that needs improvement."
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 85 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 IBM InfoSphere DataStage, 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!!