Azure Data Factory vs Upsolver comparison

Microsoft and Upsolver are both solutions in the Data Integration category. Microsoft is ranked #5 with an average rating of 8.1, while Upsolver is ranked #38 with an average rating of 8.5. Microsoft holds a 2.3% mindshare in DI, compared to Upsolver’s 0.8% mindshare. Additionally, 92% of Microsoft users are willing to recommend the solution, compared to 100% of Upsolver users who would recommend it.

Azure Data Factory

Read 96 Azure Data Factory reviews

13,392 Views
9,240 Comparison Views

92% willing to recommend

Upsolver

Read 4 Upsolver reviews

1,819 Views
1,455 Comparison Views

100% willing to recommend

Azure Data Factory

Upsolver

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Executive Summary

We performed a comparison between Azure Data Factory and Upsolver based on real PeerSpot user reviews.

Find out in this report how the two Data Integration solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.

To learn more, read our detailed Azure Data Factory vs. Upsolver Report (Updated: June 2026).

Buyer's Guide

Azure Data Factory vs. Upsolver

June 2026

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Helped 902,988 peers since 2012

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)

Upsolver

Ranking in Data Integration

38th

Average Rating

8.6

Reviews Sentiment

7.6

Number of Reviews

Ranking in other categories

Streaming Analytics (21st)

Mindshare comparison

As of July 2026, in the Data Integration category, the mindshare of Azure Data Factory is 2.3%, down from 7.6% compared to the previous year. The mindshare of Upsolver is 0.8%, up from 0.1% 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%
Upsolver	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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reviewer2784462

Software Engineer at a tech vendor with 10,001+ employees

Streaming pipelines have become simpler and onboarding new data sources is now much faster

One of the best features Upsolver offers is the automatic schema evolution. Another good feature is SQL-based streaming transformations. Complex streaming transformations such as cleansing, deduplication, and enrichment were implemented using SQL and drastically reduced the need for custom Spark code. My experience with the SQL-based streaming transformations in Upsolver is that it had a significant positive impact on the overall data engineering workflow. By replacing custom Spark streaming jobs with declarative SQL logic, I simplified development, review, and deployment processes. Data transformations such as parsing, filtering, enrichment, and deduplication could be implemented and modified quickly without rebuilding or redeploying complex code-based pipelines. Upsolver has impacted my organization positively because it brings many benefits. The first one is faster onboarding of new data sources. Another one is more reliable streaming pipelines. Another one is near-real-time data availability, which is very important for us. It also reduced operational effort for data engineering teams. A specific outcome that highlights these benefits is that the time to onboard new sources is reduced from weeks to days. Custom Spark code reduction reached 50 to 40 percent. Pipeline failures are reduced by 70 to 80 percent. Data latency is improved from hours to minutes.

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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

"Data Factory lets us consolidate those steps into a single pipeline."

"The most valuable part of this product is the ease of use, as it is easy to use and rather intuitive, and because it is easy to use, you can do things with it easily, making your work easier and therefore more valuable."

"I think it makes it very easy to understand what data flow is and so on. You can leverage the user interface to do the different data flows, and it's great. I like it a lot."

"The support for SAP services and databases, specifically SAP HANA, has been a game-changer for us."

"It makes it easy to collect data from different sources."

"For developers that are very accustomed to the Microsoft development studio, it's very easy for them to complete end-to-end data integration."

"The solution can scale very easily."

"The solution has a good interface and the integration with GitHub is very useful."

More Azure Data Factory pros

"It was easy to use and set up, with a nearly no-code interface that relied mostly on drag-and-drop functionality."

"Customer service is excellent, and I would rate it between eight point five to nine out of ten."

"The most prominent feature of Upsolver is its function as an ETL tool, allowing data to be moved across platforms and different data technologies."

"A specific outcome that highlights these benefits is that the time to onboard new sources is reduced from weeks to days, custom Spark code reduction reached 50 to 40 percent, pipeline failures are reduced by 70 to 80 percent, and data latency is improved from hours to minutes."

"I have saved 50 to 60% on maintaining pipelines since using Upsolver."

Cons

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

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

"The main challenge with implementing Azure Data Factory is that it processes data in batches, not near real-time. To achieve near real-time processing, we need to schedule updates more frequently, which can be an issue. Its interface needs to be lighter."

"As far as customer service and support with Azure Data Factory, we are not always satisfied with the response time."

"Azure Data Factory could benefit from improvements in its monitoring capabilities to provide a more robust feature set. Enhancing the ease of deployment to higher environments within Azure DevOps would be beneficial, as the current process often requires extensive scripting and pipeline development. It is also known for the flexibility of the data flow feature, particularly in supporting more dynamic data-driven architectures. These enhancements would contribute to a more seamless and efficient workflow within GitLab."

"It would be helpful if they could adjust the data capture feature so that when there are source-side changes ADF could automatically figure it out."

"I find that Azure Data Factory is still maturing, so there are issues."

"I did not see any positive impact from Azure Data Factory overall."

More Azure Data Factory cons

"Upsolver excels in ETL and data aggregation, while ThoughtSpot is strong in natural language processing for querying datasets. Combining these tools can be very effective: Upsolver handles aggregation and ETL, and ThoughtSpot allows for natural language queries. There’s potential for highlighting these integrations in the future."

"I think that Upsolver can be improved in orchestration because it is not a full orchestration tool."

"On the stability side, I would rate it seven out of ten. Using multiple cloud providers and data engineering technologies creates complexity, and managing different plugins is not always easy, but they are working on it."

"I would say Upsolver's scalability is eight out of 10 because of pricing."

"There is room for improvement in query tuning."

Pricing and Cost Advice

"The pricing model is based on usage and is not cheap."

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

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

"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 pricing is a bit on the higher end."

"The cost is based on the amount of data sets that we are ingesting."

"Data Factory is expensive."

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

More Azure Data Factory pricing and cost advice

"Upsolver is affordable at approximately $225 per terabyte per year."

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Top Industries

By visitors reading reviews

Financial Services Firm

12%

Computer Software Company

Manufacturing Company

Construction Company

Real Estate/Law Firm

15%

Manufacturing Company

15%

Retailer

11%

Construction Company

11%

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

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

No data available

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 is your experience regarding pricing and costs for Upsolver?

My experience with pricing, setup cost, and licensing is that the pricing is nine out of 10.

See all answers

What needs improvement with Upsolver?

I think Upsolver can be improved with deeper integration with external orchestration out of the box. I would appreciate more clear dashboards with billing in real time as a needed improvement.

See all answers

What is your primary use case for Upsolver?

My main use case for Upsolver is to operate with changes in the structure of new data without a pipeline disrupting. I write SQL queries in Upsolver, and the platform takes care of the data itself,...

See all answers

Comparisons

Snowflake vs Azure Data Factory

Compared 5% of the time

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

Compared 4% of the time

Databricks vs Azure Data Factory

Compared 4% of the time

Oracle Data Integrator (ODI) vs Azure Data Factory

Compared 3% of the time

Palantir Foundry vs Azure Data Factory

Compared 3% of the time

More Azure Data Factory Competitors

Databricks vs Upsolver

Compared 11% of the time

Striim vs Upsolver

Compared 10% of the time

SAS Access vs Upsolver

Compared 8% of the time

Alteryx Designer vs Upsolver

Compared 7% of the time

Rivery vs Upsolver

Compared 7% of the time

More Upsolver Competitors

Product Reports

Buyer's Guide

Azure Data Factory

June 2026

Download Azure Data Factory product report

Buyer's Guide

Data Integration

June 2026

Download Upsolver 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

Upsolver offers a data lakehouse platform that simplifies big data processing and analytics, enabling data teams to efficiently handle large-scale datasets.

Upsolver's platform focuses on simplifying the complexities of data engineering by transforming raw data into queryable formats quickly. It allows seamless integration with cloud storage and various database systems, providing flexibility for data-driven businesses. The platform automates data preparation tasks, reducing manual coding and allowing teams to focus on extracting insights. Its scalable architecture supports real-time analytics and batch processing.

What are the key features of Upsolver?

Data Ingestion: Easily ingest data from multiple sources with minimal configuration.
Schema On-Read: Automatically interpret data schemas, simplifying data management.
Streaming SQL: Transform and analyze streaming data using a familiar SQL syntax.
Data Lake Integration: Seamlessly connect with data lakes for streamlined workflows.

What benefits and ROI should users consider?

Time Savings: Faster data processing reduces time to insights.
Cost Efficiency: Scalable architecture optimizes infrastructure costs.
Scalability: Handle growing data volumes effortlessly.
Flexibility: Supports wide data storage and processing strategies.

In the e-commerce industry, Upsolver helps businesses optimize their data pipelines for better customer segmentation and personalization, while in finance, it enhances fraud detection capabilities through real-time data analytics.

Upsolver

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

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Buyer's Guide

Azure Data Factory vs. Upsolver

June 2026

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