Azure Data Factory vs StreamSets comparison

 

Comparison Buyer's Guide

Executive Summary
 

Categories and Ranking

Azure Data Factory
Ranking in Data Integration
1st
Average Rating
8.0
Number of Reviews
81
Ranking in other categories
Cloud Data Warehouse (3rd)
StreamSets
Ranking in Data Integration
8th
Average Rating
8.4
Number of Reviews
24
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of June 2024, in the Data Integration category, the mindshare of Azure Data Factory is 9.6%, down from 13.6% compared to the previous year. The mindshare of StreamSets is 1.6%, up from 1.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Integration
Unique Categories:
Cloud Data Warehouse
13.6%
No other categories found
 

Featured Reviews

Joaquin Marques - PeerSpot reviewer
Dec 1, 2022
Provides great consistency that has made implementations less buggy and less complex
I'd like to see videos on YouTube or the Microsoft site with more detailed implementations. The solution lacks a decent UI that allows us to see what kinds of requests are for what objects and how the population of objects is being requested and compared. Right now we have to look at logs to get an idea of what types of calls the data factory receives in what sequence, for example. It would be nice to be able to see it graphically because we currently have to interpret the logs and then create a graphical representation to have an idea of what's going on. In general, it could be simplified and made more user-friendly.
Avinash Mukesh - PeerSpot reviewer
May 17, 2023
User-friendly interface and easy integration, but needs easier transformation logic and faster support
When using Transformer for Snowflake, it's a bit complex to understand the transformation logic. You need someone who has some technical skills to handle it. You need to have some skills to transform the data. However, it's important that Transformer for Snowflake is a serverless engine embedded within the platform, so there is no need for maintenance. Having a serverless engine makes it easy for any enterprise to not think about or worry about the cost of maintaining the software. The data collector in StreamSets has to be designed properly. For example, a simple database configuration with MySQL DB requires the MySQL Connector to be installed.

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Pros

"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 overall performance is quite good."
"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."
"It makes it easy to collect data from different sources."
"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."
"The most valuable feature of this solution would be ease of use."
"We haven't had any issues connecting it to other products."
"Data Flow and Databricks are going to be extremely valuable services, allowing data solutions to scale as the business grows and new data sources are added."
"Important features include that it comprises lots of functionality to connect data from various sources through connector availability, scheduling pipelines at any time, and integration with third-party and security solutions for encryption."
"It is a very powerful, modern data analytics solution, in which you can integrate a large volume of data from different sources. It integrates all of the data and you can design, create, and monitor pipelines according to your requirements. It is an all-in-one day data ops solution."
"For me, the most valuable features in StreamSets have to be the Data Collector and Control Hub, but especially the Data Collector. That feature is very elegant and seamlessly works with numerous source systems."
"The scheduling within the data engineering pipeline is very much appreciated, and it has a wide range of connectors for connecting to any data sources like SQL Server, AWS, Azure, etc. We have used it with Kafka, Hadoop, and Azure Data Factory Datasets. Connecting to these systems with StreamSets is very easy."
"Also, the intuitive canvas for designing all the streams in the pipeline, along with the simplicity of the entire product are very big pluses for me. The software is very simple and straightforward. That is something that is needed right now."
"The UI is user-friendly, it doesn't require any technical know-how and we can navigate to social media or use it more easily."
"The Ease of configuration for pipes is amazing. It has a lot of connectors. Mainly, we can do everything with the data in the pipe. I really like the graphical interface too"
"The most valuable feature is the pipelines because they enable us to pull in and push out data from different sources and to manipulate and clean things up within them."
 

Cons

"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."
"Data Factory's monitorability could be better."
"The need to work more on developing out-of-the-box connectors for other products like Oracle, AWS, and others."
"The user interface could use improvement. It's not a major issue but it's something that can be improved."
"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."
"Lacks in-built streaming data processing."
"Some of the optimization techniques are not scalable."
"One area for improvement is documentation. At present, there isn't enough documentation on how to use Azure Data Factory in certain conditions. It would be good to have documentation on the various use cases."
"The execution engine could be improved. When I was at their session, they were using some obscure platform to run. There is a controller, which controls what happens on that, but you should be able to easily do this at any of the cloud services, such as Google Cloud. You shouldn't have any issues in terms of how to run it with their online development platform or design platform, basically their execution engine. There are issues with that."
"The software is very good overall. Areas for improvement are the error logging and the version history. I would like to see better, more detailed error logging information."
"There aren't enough hands-on labs, and debugging is also an issue because it takes a lot of time. Logs are not that clear when you are debugging, and you can only select a single source for a pipeline."
"In terms of the product, I don't think there is any room for improvement because it is very good. One small area of improvement that is very much needed is on the knowledge base side. Sometimes, it is not very clear how to set up a certain process or a certain node for a person who's using the platform for the first time."
"One thing that I would like to add is the ability to manually enter data. The way the solution currently works is we don't have the option to manually change the data at any point in time. Being able to do that will allow us to do everything that we want to do with our data. Sometimes, we need to manually manipulate the data to make it more accurate in case our prior bifurcation filters are not good. If we have the option to manually enter the data or make the exact iterations on the data set, that would be a good thing."
"I would like to see it integrate with other kinds of platforms, other than Java. We're going to have a lot of applications using .NET and other languages or frameworks. StreamSets is very helpful for the old Java platform but it's hard to integrate with the other platforms and frameworks."
"The logging mechanism could be improved. If I am working on a pipeline, then create a job out of it and it is running, it will generate constant logs. So, the logging mechanism could be simplified. Now, it is a bit difficult to understand and filter the logs. It takes some time."
"They need to improve their customer care services. Sometimes it has taken more than 48 hours to resolve an issue. That should be reduced. They are aware of small or generic issues, but not the more technical or deep issues. For those, they require some time, generally 48 to 72 hours to respond. That should be improved."
 

Pricing and Cost Advice

"My company is on a monthly subscription for Azure Data Factory, but it's more of a pay-as-you-go model where your monthly invoice depends on how many resources you use. On a scale of one to five, pricing for Azure Data Factory is a four. It's just the usage fees my company pays monthly."
"For our use case, it is not expensive. We take into the picture everything: resources, learning curve, and maintenance."
"There's no licensing for Azure Data Factory, they have a consumption payment model. How often you are running the service and how long that service takes to run. The price can be approximately $500 to $1,000 per month but depends on the scaling."
"I would not say that this product is overly expensive."
"The solution's fees are based on a pay-per-minute use plus the amount of data required to process."
"Azure Data Factory gives better value for the price than other solutions such as Informatica."
"It's not particularly expensive."
"Pricing appears to be reasonable in my opinion."
"StreamSets is an expensive solution."
"StreamSets is expensive, especially for small businesses."
"I believe the pricing is not equitable."
"There are two editions, Professional and Enterprise, and there is a free trial. We're using the Professional edition and it is competitively priced."
"Its pricing is pretty much up to the mark. For smaller enterprises, it could be a big price to pay at the initial stage of operations, but the moment you have the Seed B or Seed C funding and you want to scale up your operations and aren't much worried about the funds, at that point in time, you would need a solution that could be scaled."
"We are running the community version right now, which can be used free of charge."
"The licensing is expensive, and there are other costs involved too. I know from using the software that you have to buy new features whenever there are new updates, which I don't really like. But initially, it was very good."
"The pricing is too fixed. It should be based on how much data you need to process. Some businesses are not so big that they process a lot of data."
report
Use our free recommendation engine to learn which Data Integration solutions are best for your needs.
787,817 professionals have used our research since 2012.
 

Top Industries

By visitors reading reviews
Computer Software Company
13%
Financial Services Firm
13%
Manufacturing Company
8%
Healthcare Company
7%
Financial Services Firm
17%
Computer Software Company
13%
Manufacturing Company
8%
Government
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

Questions from the Community

How do you select the right cloud ETL tool?
AWS Glue and Azure Data factory for ELT best performance cloud services.
How does Azure Data Factory compare with Informatica PowerCenter?
Azure Data Factory is flexible, modular, and works well. In terms of cost, it is not too pricey. It offers the stability and reliability I am looking for, good scalability, and is easy to set up an...
How does Azure Data Factory compare with Informatica Cloud Data Integration?
Azure Data Factory is a solid product offering many transformation functions; It has pre-load and post-load transformations, allowing users to apply transformations either in code by using Power Q...
What do you like most about StreamSets?
The best thing about StreamSets is its plugins, which are very useful and work well with almost every data source. It's also easy to use, especially if you're comfortable with SQL. You can customiz...
What needs improvement with StreamSets?
We often faced problems, especially with SAP ERP. We struggled because many columns weren't integers or primary keys, which StreamSets couldn't handle. We had to restructure our data tables, which ...
What is your primary use case for StreamSets?
StreamSets is used for data transformation rather than ETL processes. It focuses on transforming data directly from sources without handling the extraction part of the process. The transformed data...
 

Learn More

Video not available
 

Overview

 

Sample Customers

1. Adobe 2. BMW 3. Coca-Cola 4. General Electric 5. Johnson & Johnson 6. LinkedIn 7. Mastercard 8. Nestle 9. Pfizer 10. Samsung 11. Siemens 12. Toyota 13. Unilever 14. Verizon 15. Walmart 16. Accenture 17. American Express 18. AT&T 19. Bank of America 20. Cisco 21. Deloitte 22. ExxonMobil 23. Ford 24. General Motors 25. IBM 26. JPMorgan Chase 27. Microsoft (Azure Data Factory is developed by Microsoft) 28. Oracle 29. Procter & Gamble 30. Salesforce 31. Shell 32. Visa
Availity, BT Group, Humana, Deluxe, GSK, RingCentral, IBM, Shell, SamTrans, State of Ohio, TalentFulfilled, TechBridge
Find out what your peers are saying about Azure Data Factory vs. StreamSets and other solutions. Updated: May 2024.
787,817 professionals have used our research since 2012.