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Azure Data Factory vs Qlik Compose comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Dec 19, 2024

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
3rd
Average Rating
8.0
Reviews Sentiment
6.8
Number of Reviews
93
Ranking in other categories
Cloud Data Warehouse (2nd)
Qlik Compose
Ranking in Data Integration
46th
Average Rating
7.6
Reviews Sentiment
6.5
Number of Reviews
12
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of February 2026, in the Data Integration category, the mindshare of Azure Data Factory is 3.0%, down from 9.8% compared to the previous year. The mindshare of Qlik Compose is 0.9%, down from 1.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Integration Market Share Distribution
ProductMarket Share (%)
Azure Data Factory3.0%
Qlik Compose0.9%
Other96.1%
Data Integration
 

Featured Reviews

KandaswamyMuthukrishnan - PeerSpot reviewer
Director at a computer software company with 1,001-5,000 employees
Integrates diverse data sources and streamlines ETL processes effectively
Regarding potential areas of improvement for Azure Data Factory, there is a need for better data transformation, especially since many people are now depending on DataBricks more for connectivity and data integration. Azure Data Factory should consider how to enhance integration or filtering for more transformations, such as integrating with Spark clusters. I am satisfied with Azure Data Factory so far, but I suggest integrating some AI functionality to analyze data during the transition itself, providing insights such as null records, common records, and duplicates without running a separate pipeline or job. The monitoring tools in Azure Data Factory are helpful for optimizing data pipelines; while the current feature is adequate, they can improve by creating a live dashboard to see the online process, including how much percentage has been completed, which will be very helpful for people who are monitoring the pipeline.
Sahil Taneja - PeerSpot reviewer
Principal Consultant/Manager at Tenzing
Easy matching and reconciliation of data
The initial setup was easy for the data warehousing concept. But for a person who is new to ETL and warehousing concepts, it may take some time. If someone is familiar with these concepts, they could understand and learn the tool quickly. However, compared to other tools, the UI is complex. It would be helpful to have a better UI and documentation for new users. As of now, there is a challenge in learning the Compose tool for new users altogether. Qlik Compose was deployed on-premises. But the servers, like the SQL servers were maintained on the cloud—the managed instances.

Quotes from Members

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

Pros

"The most valuable feature I have found at Azure Data Factory is the data flow function."
"Powerful but easy-to-use and intuitive."
"The trigger scheduling options are decently robust."
"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."
"I can do everything I want with SSIS and Azure Data Factory."
"I enjoy the ease of use for the backend JSON generator, the deployment solution, and the template management."
"The two most valuable features of Azure Data Factory are that it's very scalable and that it's also highly reliable."
"The valuable feature of Azure Data Factory is its integration capability, as it goes well with other components of Microsoft Azure."
"There were many valuable features, such as extracting any data to put in the cloud. For example, Qlik was able to gather data from SAP and extract SAP data from the platforms."
"I like modeling and code generation. It has become a pretty handy tool because of its short ideation to delivery time. From the time you decide you are modeling a data warehouse, and once you finish the modeling, it generates all the code, generates all the tables. All you have to do is tick a few things, and you can produce a fully functional warehouse. I also like that they have added all the features I have asked for over four years."
"I have found it to be a very good, stable, and strong product."
"One of the most valuable features of this tool is its automation capabilities, allowing us to design the warehouse in an automated manner. Additionally, we can generate Data Lifecycle Policies (DLP) reports and efficiently implement updates and best practices based on proven design patterns."
"One of the most valuable features was the ability to integrate multiple source systems that mainly used structured IDBMS versions."
"It is a scalable solution."
"The most valuable is its excellence as a graphical data representation tool and the versatility it offers, especially with drill-down capabilities."
"The technical support is very good. I rate the technical support a ten out of ten."
 

Cons

"While it has a range of connectors for various systems, such as ERP systems, the support for these connectors can be lacking."
"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."
"The tool’s workflow is not user-friendly. It should also improve its orchestration monitoring."
"I have encountered a problem with the integration with third-party solutions, particularly with SAP."
"There aren't many third-party extensions or plugins available in the solution."
"I would like to be informed about the changes ahead of time, so we are aware of what's coming."
"I have not found any real shortcomings within the product."
"The speed and performance need to be improved."
"There should be proper documentation available for the implementation process."
"For more complex work, we are not using Qlik Compose because it cannot handle very high volumes at the moment. It needs the same batching capabilities that other ETL tools have. We can't batch the data into small chunks when transforming large amounts of data. It tries to do everything in one shot and that's where it fails."
"When processing data from certain tables with a large volume of data, we encounter significant delays. For instance, when dealing with around one million records, it typically takes three to four hours. To address this, I aim to implement performance improvements across all tables, ensuring swift processing similar to those that are currently complete within seconds. The performance issue primarily arises when we analyze the inserts and updates from the source, subsequently dropping the table. While new insertions are handled promptly, updates are processed slowly, leading to performance issues. Despite consulting our Qlik vendors, they were unable to pinpoint the exact cause of this occurrence. Consequently, I am seeking ways to optimize performance within Qlik Compose, specifically concerning updates."
"I'd like to have access to more developer training materials."
"There is some scope for improvement around the documentation, and a better UI would definitely help."
"Qlik's ETL and data transformation could be better."
"My issues with the solution's stability are owing to the fact that it has certain bugs causing issues in some functionalities that should be working."
"There could be more customization options."
 

Pricing and Cost Advice

"For our use case, it is not expensive. We take into the picture everything: resources, learning curve, and maintenance."
"Our licensing fees are approximately 15,000 ($150 USD) per month."
"The cost is based on the amount of data sets that we are ingesting."
"Pricing appears to be reasonable in my opinion."
"Azure products generally offer competitive pricing, suitable for diverse budget considerations."
"The solution's fees are based on a pay-per-minute use plus the amount of data required to process."
"The pricing model is based on usage and is not cheap."
"The price is fair."
"On a scale of one to ten, where one is cheap, and ten is very expensive, I rate the solution a six."
"While they outperform Tableau, there's room for improvement in Qlik's pricing structures, especially for corporate clients like us."
"The price of the solution is expensive."
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Top Industries

By visitors reading reviews
Financial Services Firm
13%
Computer Software Company
11%
Manufacturing Company
9%
Government
6%
Financial Services Firm
15%
Government
13%
Manufacturing Company
10%
Insurance Company
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business31
Midsize Enterprise19
Large Enterprise57
By reviewers
Company SizeCount
Small Business3
Midsize Enterprise3
Large Enterprise6
 

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...
Which ETL tool would you recommend to populate data from OLTP to OLAP?
There are two products I know about * TimeXtender : Microsoft based, Transformation logic is quiet good and can easily be extended with T-SQL , Has a semantic layer that generates metat data for cu...
 

Also Known As

No data available
Compose, Attunity Compose
 

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
Poly-Wood
Find out what your peers are saying about Azure Data Factory vs. Qlik Compose and other solutions. Updated: February 2026.
881,733 professionals have used our research since 2012.