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.
"Data Factory's best feature is the ease of setting up pipelines for data and cloud integrations."
"One advantage of Azure Data Factory is that it's fast, unlike SSIS and other on-premise tools. It's also very convenient because it has multiple connectors. The availability of native connectors allows you to connect to several resources to analyze data streams."
"From what we have seen so far, the solution seems very stable."
"In terms of my personal experience, it works fine."
"The most valuable feature of this solution would be ease of use."
"We have found the bulk load feature very valuable."
"The most valuable feature of Azure Data Factory is the core features that help you through the whole Azure pipeline or value chain."
"I think it makes it very easy to understand what data flow is and so on. You can leverage the user interface to do the different data flows, and it's great. I like it a lot."
"The most valuable feature of the solution is the analytics and that it can connect with Power BI."
"The most important feature for me is the integration with PolyBase."
"I have been working with Microsoft, and they have been very helpful."
"The most valuable aspect of this Microsoft Azure Synapse Analytics is its consolidation of technical support from Microsoft, and its ability to securely host large quantities of data within the cloud environment. The overall ability to manage and maintain Big Data within the cloud provides a heightened level of efficiency, reliability, and support from Microsoft. This results in a superior user experience and an increased level of value for the end user."
"The architecture, including compute and storage, is good."
"We can have the dedicated SQL up and running within 15 minutes."
"The most valuable features of Microsoft Azure Synapse Analytics are how easy and quick it is to set up the linked services."
"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 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."
"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."
"The solution should offer better integration with Azure machine learning. We should be able to embed the cognitive services from Microsoft, for example as a web API. It should allow us to embed Azure machine learning in a more user-friendly way."
"On the UI side, they could make it a little more intuitive in terms of how to add the radius components. Somebody who has been working with tools like Informatica or DataStage gets very used to how the UI looks and feels."
"Some of the optimization techniques are not scalable."
"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."
"If the user interface was more user friendly and there was better error feedback, it would be helpful."
"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."
"It would be beneficial to take the top vendors and identify some kind of straightforward action to work with them. Instead of having to employ a separate vendor tool to be able to move this, it would be nice to be able to go through Microsoft."
"The solution does not support oriented scaling in the synapse."
"Synapse Analytics is generally stable, but its performance can be slow when performing very large datasets."
"I would like my team to be able to build pipelines that integrate with the Azure Data Factory."
"Scaling this solution up and down is not quick and easy. This could be improved. The pricing of this solution could also be improved."
"It could be more stable."
"It would be ideal if the solution could be better used intuitively by the staff without having a great deal of training."
"There may be some challenges in terms of connecting with Virtual Networks (VNETs) to Microsoft Azure Synapse Analytics."
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 Palantir Foundry, 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!!