Azure Data Factory vs StreamSets comparison

Microsoft and IBM are both solutions in the Data Integration category. Microsoft is ranked #1 with an average rating of 8.1, while IBM is ranked #9 with an average rating of 8.4. Microsoft holds a 11.0% mindshare in DI, compared to IBM’s 1.8% mindshare. Additionally, 92% of Microsoft users are willing to recommend the solution, compared to 100% of IBM users who would recommend it.

Azure Data Factory

Read 86 Azure Data Factory reviews

21,615 Views
17,232 Comparison Views

92% willing to recommend

StreamSets

Read 22 StreamSets reviews

2,905 Views
1,916 Comparison Views

100% willing to recommend

Azure Data Factory

StreamSets

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Executive SummaryUpdated on Dec 19, 2024

Azure Data Factory and StreamSets compete in the data integration and ETL tools category. Azure Data Factory has the upper hand due to its integration within the Microsoft ecosystem and extensive built-in connectors.

Features: StreamSets offers no-code ETL capabilities, ideal for non-technical users, supports batch and streaming with data drift resilience and Change Data Capture. Azure Data Factory provides robust data transformation and integration with over 100 built-in connectors, favoring users with ETL process knowledge.

Room for Improvement: StreamSets faces integration challenges outside Java-based platforms and has a steep learning curve. Users raise concerns about logging and memory management. Azure Data Factory's complex pricing, limited streaming capabilities, and non-Microsoft platform integration require improvement. Documentation clarity and non-native system support are frequently mentioned.

Ease of Deployment and Customer Service: StreamSets is noted for excellent customer support but has a higher initial deployment effort due to its learning curve. It operates in public and hybrid cloud environments. Azure Data Factory leverages Microsoft's cloud infrastructure, easing deployment but complicating Azure resource management, often relying on community support.

Pricing and ROI: StreamSets offers diverse pricing from open source to enterprise, with higher costs for smaller businesses but notable ROI by reducing data drift issues. Azure Data Factory's pay-as-you-go model poses estimation challenges and can become costly with extensive use. Competitive pricing is noted, emphasizing careful management to prevent unexpected expenses.

To learn more, read our detailed Azure Data Factory vs. StreamSets Report (Updated: December 2024).

Buyer's Guide

Azure Data Factory vs. StreamSets

December 2024

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Categories and Ranking

Azure Data Factory

Ranking in Data Integration

1st

Average Rating

8.0

Reviews Sentiment

6.9

Number of Reviews

Ranking in other categories

Cloud Data Warehouse (3rd)

StreamSets

Ranking in Data Integration

9th

Average Rating

8.4

Reviews Sentiment

7.1

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of December 2024, in the Data Integration category, the mindshare of Azure Data Factory is 11.0%, down from 13.3% compared to the previous year. The mindshare of StreamSets is 1.8%, up from 1.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Integration

Featured Reviews

Thulani David Mngadi

Data architect at Old Mutual

Data flow feature is valuable for data transformation tasks

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.

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Reyansh Kumar

Technical Specialist at Accenture

We no longer need to hire highly skilled data engineers to create and monitor data pipelines

The things I like about StreamSets are its * overall user interface * efficiency * product features, which are all good. Also, 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. You just need to configure the data sources, the paths and their configurations, and you are ready to go. It is very efficient and very easy to use for ETL pipelines. It is a GUI-based interface in which you can easily create or design your own data pipelines with just a few clicks. As for moving data into modern analytics systems, we are using it with Microsoft Power BI, AWS, and some on-premises solutions, and it is very easy to get data from StreamSets into them. No hardcore coding or special technical expertise is required. It is also a no-code platform in which you can configure your data sources and data output for easy configuration of your data pipeline. This is a very important aspect because if a tool requires code development, we need to hire software developers to get the task done. By using StreamSets, it can be done with a few clicks.

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Quotes from Members

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

Pros

"An excellent tool for pipeline orchestration."

"It is beneficial that the solution is written with Spark as the back end."

"The most valuable features are data transformations."

"Powerful but easy-to-use and intuitive."

"The most valuable feature of Azure Data Factory is that it has a good combination of flexibility, fine-tuning, automation, and good monitoring."

"The data factory agent is quite good and programming or defining the value of jobs, processes, and activities is easy."

"We have been using drivers to connect to various data sets and consume data."

"It is easy to deploy workflows and schedule jobs."

More Azure Data Factory pros

"StreamSets’ data drift resilience has reduced the time it takes us to fix data drift breakages. For example, in our previous Hadoop scenario, when we were creating the Sqoop-based processes to move data from source to destinations, we were getting the job done. That took approximately an hour to an hour and a half when we did it with Hadoop. However, with the StreamSets, since it works on a data collector-based mechanism, it completes the same process in 15 minutes of time. Therefore, it has saved us around 45 minutes per data pipeline or table that we migrate. Thus, it reduced the data transfer, including the drift part, by 45 minutes."

"It is really easy to set up and the interface is easy to use."

"The entire user interface is very simple and the simplicity of creating pipelines is something that I like very much about it. The design experience is very smooth."

"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 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 ability to have a good bifurcation rate and fewer mistakes is valuable."

"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 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."

More StreamSets pros

Cons

"There's space for improvement in the development process of the data pipelines."

"There is no built-in function for automatically adding notifications concerning the progress or outline of a pipeline run."

"Currently, our company requires a monitoring tool, and that isn't available in Azure Data Factory."

"Lacks in-built streaming data processing."

"You cannot use a custom data delimiter, which means that you have problems receiving data in certain formats."

"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 number of standard adaptors could be extended further."

"When the record fails, it's tough to identify and log."

More Azure Data Factory cons

"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."

"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."

"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."

"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 was painful. Also, pipeline failures were common, and data drifting wasn't addressed, which made things worse. Licensing was another issue we encountered."

"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."

"Sometimes, it is not clear at first how to set up nodes. A site with an explanation of how each node works would be very helpful."

"Using ETL pipelines is a bit complicated and requires some technical aid."

"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."

More StreamSets cons

Pricing and Cost Advice

"The price is fair."

"The licensing model for Azure Data Factory is good because you won't have to overpay. Pricing-wise, the solution is a five out of ten. It was not expensive, and it was not cheap."

"Azure Data Factory gives better value for the price than other solutions such as Informatica."

"The solution's fees are based on a pay-per-minute use plus the amount of data required to process."

"The price you pay is determined by how much you use it."

"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 am aware of the pricing of Azure Data Factory, but I prefer not to disclose specific details."

"I would not say that this product is overly expensive."

More Azure Data Factory pricing and cost advice

"I believe the pricing is not equitable."

"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."

"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."

"The pricing is affordable for any business."

"We use the free version. It's great for a public, free release. Our stance is that the paid support model is too expensive to get into. They should honestly reevaluate that."

"It's not so favorable for small companies."

"The overall cost is very flexible so it is not a burden for our organization... However, the cost should be improved. For small and mid-size organizations it might be a challenge."

"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."

More StreamSets pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

13%

Computer Software Company

12%

Manufacturing Company

Healthcare Company

Financial Services Firm

17%

Computer Software Company

10%

Manufacturing Company

10%

Insurance Company

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.

See all answers

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...

See all answers

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...

See all answers

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...

See all answers

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 ...

See all answers

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...

See all answers

Comparisons

Informatica PowerCenter vs Azure Data Factory

Compared 13% of the time

Snowflake vs Azure Data Factory

Compared 10% of the time

Informatica Intelligent Data Management Cloud (IDMC) vs Azure Data Factory

Compared 8% of the time

Alteryx Designer vs Azure Data Factory

Compared 7% of the time

IBM InfoSphere DataStage vs Azure Data Factory

Compared 4% of the time

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Informatica PowerCenter vs StreamSets

Compared 13% of the time

IBM InfoSphere DataStage vs StreamSets

Compared 10% of the time

Fivetran vs StreamSets

Compared 10% of the time

webMethods.io vs StreamSets

Compared 8% of the time

SSIS vs StreamSets

Compared 5% of the time

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Product Reports

Buyer's Guide

Azure Data Factory

December 2024

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Buyer's Guide

StreamSets

November 2024

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Microsoft

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IBM

Overview

Azure Data Factory efficiently manages and integrates data from various sources, enabling seamless movement and transformation across platforms. Its valuable features include seamless integration with Azure services, handling large data volumes, flexible transformation, user-friendly interface, extensive connectors, and scalability. Users have experienced improved team performance, workflow simplification, enhanced collaboration, streamlined processes, and boosted productivity.

StreamSets is a data integration platform that enables organizations to efficiently move and process data across various systems. It offers a user-friendly interface for designing, deploying, and managing data pipelines, allowing users to easily connect to various data sources and destinations. StreamSets also provides real-time monitoring and alerting capabilities, ensuring that data is flowing smoothly and any issues are quickly addressed.

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

Buyer's Guide

Azure Data Factory vs. StreamSets

December 2024

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