Azure Data Factory vs Qlik Compose comparison

Microsoft and Qlik are both solutions in the Data Integration category. Microsoft is ranked #5 with an average rating of 7.9, while Qlik is ranked #52. Microsoft holds a 2.3% mindshare in DI, compared to Qlik’s 0.8% mindshare. Additionally, 92% of Microsoft users are willing to recommend the solution, compared to 81% of Qlik users who would recommend it.

Azure Data Factory

Read 96 Azure Data Factory reviews

14,099 Views
9,728 Comparison Views

92% willing to recommend

Qlik Compose

Read 12 Qlik Compose reviews

2,621 Views
2,123 Comparison Views

81% willing to recommend

Azure Data Factory

Qlik Compose

Comparison Buyer's Guide

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Executive SummaryUpdated on Dec 19, 2024

Qlik Compose and Azure Data Factory are both powerful tools in the category of data integration and transformation. Despite Qlik Compose's appealing pricing and support, Azure Data Factory often emerges as the preferred choice due to its robust features and superior scalability.

Features: Qlik Compose offers data warehouse automation, change data capture, and lifecycle management, enabling accelerated deployment of analytics-ready data. Azure Data Factory shines with its extensive data orchestration and transformation tools, seamless integration with various cloud services, and over 100 out-of-the-box connectors supporting diverse data sources. It is also recognized for its flexibility and the ability to handle complex data flows, making it ideal for large-scale enterprise applications.

Room for Improvement: Qlik Compose could enhance its real-time processing capabilities and expand its cloud integration options to better compete with Azure's flexibility. Additionally, improved scalability features could help it cater to more extensive and complex deployments. On the other hand, Azure Data Factory users desire improvements in interface speed and more advanced monitoring features. Some users also find that its pricing model can become costly, possibly limiting its appeal for smaller companies with budget constraints.

Ease of Deployment and Customer Service: Qlik Compose's deployment is user-friendly with a focus on model-driven pipelines, beneficial for teams with limited technical expertise. It is also noted for its reliable support system. Azure Data Factory, offering detailed documentation and community support, suits intricate deployments across diverse environments. While its setup may be complex, these resources aid in troubleshooting and ensure a smooth deployment process.

Pricing and ROI: Qlik Compose is praised for its straightforward and cost-effective pricing, aligning with its automated functionality. Azure Data Factory, though perceived as more expensive due to its extensive capabilities, is considered worth the investment for those needing advanced and scalable solutions, offering significant ROI for enterprises with comprehensive data strategy requirements.

To learn more, read our detailed Azure Data Factory vs. Qlik Compose Report (Updated: August 2026).

Buyer's Guide

Azure Data Factory vs. Qlik Compose

August 2026

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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

5th

Average Rating

8.0

Reviews Sentiment

6.7

Number of Reviews

Ranking in other categories

Cloud Data Warehouse (7th)

Qlik Compose

Ranking in Data Integration

52nd

Average Rating

7.6

Reviews Sentiment

6.5

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of August 2026, in the Data Integration category, the mindshare of Azure Data Factory is 2.3%, down from 7.2% compared to the previous year. The mindshare of Qlik Compose is 0.8%, down from 1.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Integration Mindshare Distribution
Product	Mindshare (%)
Azure Data Factory	2.3%
Qlik Compose	0.8%
Other	96.9%

Data Integration

Featured Reviews

KandaswamyMuthukrishnan

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.

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Susheel A

Director - Metrics & Analytics at a computer software company with 1,001-5,000 employees

Efficient data warehouse automation with robust features, but may require enhancements in user-friendly self-service options and pricing flexibility for broader corporate appeal

It could enhance its capabilities in the realm of self-service options as currently, it is more suited for individuals with technical proficiency who can create pages using it. When it comes to end users who may lack technical expertise, they are limited to toggling between existing developments. To empower end users to make critical changes without relying heavily on technical expertise, it would be beneficial to introduce more user-friendly features for development and modification. If it could incorporate correlation analysis capabilities into its platform, especially in a user-friendly manner, it would greatly enhance the tool's overall utility and make it an even more outstanding solution. There is a room for improvement regarding stability.

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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

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

"We have found the bulk load feature very valuable."

"The most valuable feature of this solution is that it allows more data between on-premises and cloud solutions."

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

"Azure Data Factory is a low code, no code platform, which is helpful."

"The most valuable feature is the ease in which you can create an ETL pipeline."

"In terms of my personal experience, it works fine."

"Azure Data Factory was not difficult to deploy because it is a small area, so we completed it very quickly."

More Azure Data Factory pros

"It is a scalable solution."

"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."

"Qlik Compose is good enough. It is user-friendly and intuitive."

"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."

"The most valuable is its excellence as a graphical data representation tool and the versatility it offers, especially with drill-down capabilities."

"It's a stable solution."

"It can scale."

"I have found it to be a very good, stable, and strong product."

More Qlik Compose pros

Cons

"We are too early into the entire cycle for us to really comment on what problems we face. We're mostly using it for transformations, like ETL tasks. I think we are comfortable with the facts or the facts setting. But for other parts, it is too early to comment on."

"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 support and the documentation can be improved."

"It's a good idea to take a Microsoft course. Because they are really helpful when you start from your journey with Data Factory."

"I would like to be informed about the changes ahead of time, so we are aware of what's coming."

"The product could provide more ways to import and export data."

"From my perspective, the pricing seems like it could be more user-friendly."

"The pricing scheme is very complex and difficult to understand."

More Azure Data Factory cons

"I don't think Qlik can be used in a high-volume scenario. It didn't work for us."

"It would be better if the first level of technical support were a bit more technically knowledgeable to solve the problem. I think they could also improve the injection of custom scripts. It is pretty difficult to add additional scripts. If the modeling doesn't give you what you want, and you want to change the script generated by the modeling, it is a bit more challenging than in most other products. It is very good with standard form type systems, but if you get a more complicated data paradigm, it tends to struggle with transforming that into a model."

"There should be proper documentation available for the implementation process."

"I'd like to have access to more developer training materials."

"Qlik's ETL and data transformation could be better."

"It could enhance its capabilities in the realm of self-service options as currently, it is more suited for individuals with technical proficiency who can create pages using it."

"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."

"I believe that visual data flow management and the transformation function should be improved."

More Qlik Compose cons

Pricing and Cost Advice

"Pricing appears to be reasonable in my opinion."

"It's not particularly expensive."

"I don't see a cost; it appears to be included in general support."

"I would rate Data Factory's pricing nine out of ten."

"The pricing is a bit on the higher end."

"The solution is cheap."

"Pricing is comparable, it's somewhere in the middle."

"Data Factory is expensive."

More Azure Data Factory pricing and cost advice

"The price of the solution is expensive."

"While they outperform Tableau, there's room for improvement in Qlik's pricing structures, especially for corporate clients like us."

"On a scale of one to ten, where one is cheap, and ten is very expensive, I rate the solution a six."

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Top Industries

By visitors reading reviews

Financial Services Firm

12%

Manufacturing Company

Computer Software Company

Construction Company

Financial Services Firm

13%

Comms Service Provider

10%

Construction Company

10%

Manufacturing Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	31
Midsize Enterprise	21
Large Enterprise	63

By reviewers
Company Size	Count
Small Business	3
Midsize Enterprise	3
Large Enterprise	6

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

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...

See all answers

Comparisons

Snowflake vs Azure Data Factory

Compared 4% of the time

Databricks vs Azure Data Factory

Compared 4% of the time

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

Compared 4% of the time

Palantir Foundry vs Azure Data Factory

Compared 3% of the time

Oracle Data Integrator (ODI) vs Azure Data Factory

Compared 3% of the time

More Azure Data Factory Competitors

Qlik Replicate vs Qlik Compose

Compared 10% of the time

AWS Database Migration Service vs Qlik Compose

Compared 9% of the time

SSIS vs Qlik Compose

Compared 8% of the time

Qlik Talend Cloud vs Qlik Compose

Compared 8% of the time

WhereScape RED vs Qlik Compose

Compared 6% of the time

More Qlik Compose Competitors

Product Reports

Buyer's Guide

Azure Data Factory

August 2026

Download Azure Data Factory product report

Buyer's Guide

Qlik Compose

July 2026

Download Qlik Compose product report

Also Known As

No data available

Compose, Attunity Compose

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

Qlik Compose enhances data management with ETL capabilities, data integration with CDC, and real-time replication. Its intuitive interface enables easy data modeling and automation, supporting various database integrations.

Qlik Compose is designed for efficient data preparation, migration, and warehouse generation. It incorporates ETL functionalities and data integration with CDC, allowing users to effortlessly create data marts without code. Its automation features facilitate warehouse design while graphical representations and connectivity options increase versatility across multiple sources. Users value its stability, scalability, and the support provided, although improvements in ETL functionalities, performance for large datasets, and NoSQL integration are needed. The solution is widely used in business intelligence with tools like Qlik Replicate for real-time data replication and requires direct SQL for complex transformations.

What are the key features of Qlik Compose?

ETL Capabilities: Simplifies data extraction, transformation, and loading
Data Integration with CDC: Enables real-time data replication
Intuitive Interface: Facilitates easy data modeling and automation
Drag-and-Drop Functionality: Streamlines data task management
Auto-Mapping: Enhances integration of multiple data sources
No-Code Data Marts Creation: Reduces dependency on technical skills

What benefits should users consider in the reviews?

Stability and Scalability: Reliable functioning for growing data needs
Technical Support: Availability of assistance for troubleshooting
Real-Time Replication Capabilities: Efficient data processing and analytics
Low-Code Development: Simplifies complex data handling processes

In industries, Qlik Compose is implemented for enhancing data preparation, migration, and warehousing. It is used in small projects or straightforward data integration tasks and supports business intelligence efforts by offering low-code capabilities, enabling companies to deliver tailored analytics solutions efficiently.

Qlik

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

Buyer's Guide

Azure Data Factory vs. Qlik Compose

August 2026

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