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 8.0, while IBM is ranked #15 with an average rating of 8.4. Microsoft holds a 10.0% mindshare in DI, compared to IBM’s 1.6% 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 90 Azure Data Factory reviews

17,973 Views
14,229 Comparison Views

92% willing to recommend

StreamSets

Read 20 StreamSets reviews

2,525 Views
1,707 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: March 2025).

Buyer's Guide

Azure Data Factory vs. StreamSets

March 2025

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Review summaries and opinions

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

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

15th

Average Rating

8.4

Reviews Sentiment

7.1

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of March 2025, in the Data Integration category, the mindshare of Azure Data Factory is 10.0%, down from 12.9% compared to the previous year. The mindshare of StreamSets is 1.6%, up from 1.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Integration

Featured Reviews

Joy Maitra

Sr. Technical Architect at Hexaware Technologies Limited

Facilitates seamless data pipeline creation with good analytics and and thorough monitoring

Azure Data Factory is a low code, no code platform, which is helpful. It provides many prebuilt functionalities that assist in building data pipelines. Also, it facilitates easy transformation with all required functionalities for analytics. Furthermore, it connects to different sources out-of-the-box, making integration much easier. The monitoring is very thorough, though a more readable version would be appreciable.

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Nantabo Jackie

Sales Manager at Soft Hostings Limited

Simplified pipelines and helped us break down data silos within our organization

The design experience when implementing batch streaming or ECL pipelines is very easy and straightforward. When we initially attempted to integrate StreamSets with Kafka, it was somewhat challenging until we consulted the documentation, after which it became straightforward. We use StreamSets to move data into modern analytics platforms. Moving the data into modern analytics platforms is still complex. It requires a lot of understanding of logic. StreamSets enables us to build data pipelines without knowing how to code. StreamSets' ability to build data pipelines without requiring us to know complex programming is very important, as it allows us to focus on our projects without spending time writing code. StreamSets' Transformer for Snowflake is simple to use for designing both simple and complex transformation logic. StreamSets' Transformer for Snowflake is extremely important to me as it helps me to connect external data sources and keep my internal workflow organized. Transformer for Snowflake's functionality is a perfect ten out of ten. It is important and cost-effective that Transformer for Snowflake is a serverless engine embedded within the platform, as without this feature, it would be very expensive. This feature helps us to sell at lower budget costs, which would otherwise be at a high cost with other servers. StreamSets has helped improve our organization. StreamSets simplified pipelines for our organization. It is easier to complete a project when we know where and how to start, and working with the team remotely makes it more efficient. This helps us to save time and be more organized when creating data pipelines. Being a structured company that produces reliable resources for our application benefits both our clients and contacts. StreamSets' built-in data drift resilience plays a part in our ETL operations. With prior knowledge, the built-in data drift resilience is very effective, but it can be challenging to implement without the preexisting knowledge. The built-in data drift resilience reduced the time it takes us to fix data drift breakages by 45 percent. StreamSets helped us break down data silos within our organization. The use of StreamSets to break down data silos enabled us to be confident in the services and products we provide, as well as the real-time streaming we offer. This has had a positive impact on our business, as it allowed us to accurately determine the analytics we need to present to stakeholders, clients, and our sources while ensuring that the process is secure and transparent. StreamSets saved us time because anyone can use StreamSets not just developers. We can save around 40 percent of our time. StreamSets' reusable assets helped us reduce workload by around 25 percent. StreamSets saved us money by not having to hire developers with specialized skills. We saved around $2,000 US. StreamSets helped us scale our data operations. Since StreamSets makes it easy to scale our data operations, it enabled us to know exactly where to start at any time. We are aware of the timeline for completing the project, and depending on our familiarity with the software, we can come up with a solution quickly.

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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

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

"It has built-in connectors for more than 100 sources and onboarding data from many different sources to the cloud environment."

"The interface of Azure Data Factory is very usable with a more interactive visual experience, making it easier for people who are not as experienced in coding to work with."

"The scalability of the product is impressive."

"The valuable feature of Azure Data Factory is its integration capability, as it goes well with other components of Microsoft Azure."

"I can do everything I want with SSIS and Azure Data Factory."

"The most valuable feature of Azure Data Factory is the core features that help you through the whole Azure pipeline or value chain."

More Azure Data Factory pros

"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."

"StreamSets data drift feature gives us an alert upfront so we know that the data can be ingested. Whatever the schema or data type changes, it lands automatically into the data lake without any intervention from us, but then that information is crucial to fix for downstream pipelines, which process the data into models, like Tableau and Power BI models. This is actually very useful for us. We are already seeing benefits. Our pipelines used to break when there were data drift changes, then we needed to spend about a week fixing it. Right now, we are saving one to two weeks. Though, it depends on the complexity of the pipeline, we are definitely seeing a lot of time being saved."

"The best feature that I really like is the integration."

"The ETL capabilities are very useful for us. We extract and transform data from multiple data sources, into a single, consistent data store, and then we put it in our systems. We typically use it to connect our Apache Kafka with data lakes. That process is smooth and saves us a lot of time in our production systems."

"What I love the most is that StreamSets is very light. It's a containerized application. It's easy to use with Docker. If you are a large organization, it's very easy to use Kubernetes."

"StreamSets Transformer is a good feature because it helps you when you are developing applications and when you don't want to write a lot of code. That is the best feature overall."

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

More StreamSets pros

Cons

"The initial setup is not very straightforward."

"Some prebuilt data source or data connection aspects are generic."

"It does not appear to be as rich as other ETL tools. It has very limited capabilities."

"Lacks in-built streaming data processing."

"The solution needs to be more connectable to its own services."

"The support and the documentation can be improved."

"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."

"My only problem is the seamless connectivity with various other databases, for example, SAP."

More Azure Data Factory cons

"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."

"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."

"StreamSet works great for batch processing but we are looking for something that is more real-time. We need latency in numbers below milliseconds."

"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."

"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."

"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."

"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 documentation is inadequate and has room for improvement because the technical support does not regularly update their documentation or the knowledge base."

More StreamSets cons

Pricing and Cost Advice

"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."

"It seems very low initially, but as the data grows, the solution’s bills grow exponentially."

"Data Factory is expensive."

"The solution is cheap."

"Pricing appears to be reasonable in my opinion."

"Product is priced at the market standard."

"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."

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

More Azure Data Factory pricing and cost advice

"The pricing is affordable for any business."

"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."

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

"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."

"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."

"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."

"There are different versions of the product. One is the corporate license version, and the other one is the open-source or free version. I have been using the corporate license version, but they have recently launched a new open-source version so that anybody can create an account and use it. The licensing cost varies from customer to customer. I don't have a lot of input on that. It is taken care of by PMO, and they seem fine with its pricing model. It is being used enterprise-wide. They seem to have got a good deal for StreamSets."

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

14%

Computer Software Company

11%

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...

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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 12% of the time

Snowflake vs Azure Data Factory

Compared 10% of the time

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

Compared 7% of the time

Alteryx Designer vs Azure Data Factory

Compared 7% of the time

Oracle Data Integrator (ODI) vs Azure Data Factory

Compared 5% of the time

More Azure Data Factory Competitors

Informatica PowerCenter vs StreamSets

Compared 14% of the time

IBM InfoSphere DataStage vs StreamSets

Compared 12% of the time

webMethods.io vs StreamSets

Compared 9% of the time

Fivetran vs StreamSets

Compared 8% of the time

SSIS vs StreamSets

Compared 7% of the time

More StreamSets Competitors

Product Reports

Buyer's Guide

Azure Data Factory

March 2025

Download Azure Data Factory product report

Buyer's Guide

StreamSets

March 2025

Download StreamSets product report

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.

Microsoft

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.

IBM

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

March 2025

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