Azure Data Factory vs Striim comparison

Microsoft and Striim are both solutions in the Data Integration category. Microsoft is ranked #5 with an average rating of 7.9, while Striim is ranked #47 with an average rating of 8.0. Microsoft holds a 2.3% mindshare in DI, compared to Striim’s 0.6% mindshare. Additionally, 92% of Microsoft users are willing to recommend the solution, compared to 100% of Striim users who would recommend it.

Azure Data Factory

Read 97 Azure Data Factory reviews

14,099 Views
9,728 Comparison Views

92% willing to recommend

Striim

Read 2 Striim reviews

1,932 Views
1,198 Comparison Views

100% willing to recommend

Azure Data Factory

Striim

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Executive Summary

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

Find out what your peers are saying about Informatica, Microsoft, Palantir and others in Data Integration.

To learn more, read our detailed Data Integration Report (Updated: August 2026).

Buyer's Guide

Data Integration

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)

Striim

Ranking in Data Integration

47th

Average Rating

8.0

Reviews Sentiment

6.2

Number of Reviews

Ranking in other categories

Cloud Data Integration (27th), Streaming Analytics (23rd)

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 Striim is 0.6%, up from 0.3% 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%
Striim	0.6%
Other	97.1%

Data Integration

Featured Reviews

Kunal Das

Test Engineer at Happiest Minds Technologies

Drag-and-drop pipelines have saved days of work and now automate data movement and backfilling

If the AI features were more improved so that I don't have to provide each and every detail, Azure Data Factory could be improved in a much better way by improving the AI features. For example, if I want to fetch any data from a raw source, I need to provide each and every detail. But if I am just uploading my raw data and if AI will sync with that data, it can analyze that data and give me proper suggestions on how that should be done in a proper way. Automatic suggestions could improve in a much better way. As I have mentioned, the AI features as well as more drag-and-drop activities could be improved. If I am making a pipeline, it should give me suggestions, such as which activity should be used, so that I don't have to remember each activity. If I have used one activity, I shouldn't have to remember what activity should I use next. It should give auto-suggestions. That is why I have given a nine out of 10. Currently, I don't know about its governance and security, but in view of its improvement, I think Azure Data Factory should improve in these areas. As I already mentioned, the AI features should be improved. Also, the auto-suggestion features should also improve.

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Ramakanth Vanga

Data Engineer

Real-time data capture has accelerated releases and now improves trust in our data warehouse

The checkpoints would help me to figure out where the problem was if there's any lag, but I had to do a lot of manual work to figure out where the lag is. Striim would not intuitively tell me the culprit table or database behind the lag. I believe that is an improvement Striim could definitely do. Passwords were an issue. Property variables were not supported for passwords, meaning I had to make sure the password is manually populated. I believe if Striim could read from AWS secrets or its own secret mechanism to store the password, that would really save a lot of time so that I don't have to keep updating the password whenever there is any change. The user experience of triggering alerts if there's any lag which Striim identified, which is outside normal processing time, could intrinsically be done by Striim. I believe that was lacking. I would wait for Striim to tell me, instead of me going and validating whether Striim is lagging behind. If Striim could itself tell me that it's seeing a lot more volume than expected, that would really make me give it a higher number.

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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

"We haven't had any issues connecting it to other products."

"I like how you can create your own pipeline in your space and reuse those creations. You can collaborate with other people who want to use your code."

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

"The platform excels in data transformation with its user-friendly interface and robust monitoring capabilities, making ETL processes seamless."

"Azure Data Factory is great because it's a cloud service; you do not have to take care of the installation and configuration yourself."

"I enjoy the ease of use for the backend JSON generator, the deployment solution, and the template management."

"The most valuable feature of this solution would be ease of use."

"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."

More Azure Data Factory pros

"We were confidently in a situation to call Snowflake as a single source of truth, and I believe with Striim, we were able to do that because without Striim, the SLA would be much higher, and there would not have been much confidence in Snowflake."

"Striim is capable of absorbing a large number of transactions, and the difference between the two databases is always less than a second, which demonstrates efficiency and highlights the variety of sources and targets I have used."

Cons

"Data Factory could be improved by eliminating the need for a physical data area. We have to extract data using Data Factory, then create a staging database for it with Azure SQL, which is very, very expensive. Another improvement would be lowering the licensing cost."

"Data Factory's performance during heavy data processing isn't great."

"There is room for improvement primarily in its streaming capabilities. For structured streaming and machine learning model implementation within an ETL process, it lags behind tools like Informatica."

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

"Data Factory's monitorability could be better."

"Azure Data Factory should be cheaper to move data to a data center abroad for calamities in case of disasters."

"Lacks in-built streaming data processing."

"We require Azure Data Factory to be able to connect to Google Analytics."

More Azure Data Factory cons

"I think Striim could be improved with better pricing and enhanced documentation."

"The user experience of triggering alerts if there's any lag which Striim identified, which is not normal, could intrinsically be done by Striim."

Pricing and Cost Advice

"The price is fair."

"I rate the product price as six on a scale of one to ten, where one is low price and ten is high price."

"For our use case, it is not expensive. We take into the picture everything: resources, learning curve, and maintenance."

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

"This is a cost-effective solution."

"Azure products generally offer competitive pricing, suitable for diverse budget considerations."

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

"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."

More Azure Data Factory pricing and cost advice

Information not available

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Top Industries

By visitors reading reviews

Financial Services Firm

11%

Manufacturing Company

Computer Software Company

Construction Company

16%

Healthcare Company

14%

Retailer

12%

Financial Services Firm

12%

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	31
Midsize Enterprise	21
Large Enterprise	64

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 Striim?

My experience with the pricing, implementation cost, and licensing of Striim is that it is somewhat expensive.

See all answers

What needs improvement with Striim?

I think Striim could be improved with better pricing and enhanced documentation.

See all answers

What is your primary use case for Striim?

I use Striim to perform change data capture from relational databases to non-relational databases in my organization. I implement CDC with Striim by transferring data from Oracle Database to MongoD...

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 Talend Cloud vs Striim

Compared 14% of the time

Upsolver vs Striim

Compared 10% of the time

Oracle GoldenGate vs Striim

Compared 10% of the time

AWS Database Migration Service vs Striim

Compared 10% of the time

Confluent vs Striim

Compared 9% of the time

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Product Reports

Buyer's Guide

Azure Data Factory

August 2026

Download Azure Data Factory product report

Buyer's Guide

Streaming Analytics

August 2026

Download Striim product report

Also Known As

No data available

Striim Platform

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

Striim offers a comprehensive platform for real-time data integration and streaming analytics, designed to streamline data processes for enterprise-level solutions.

Striim enables seamless migration and integration of data across cloud and on-premises environments, making it ideal for businesses looking to leverage real-time analytics. Its capabilities support continuous data flow, reducing latency and enhancing decision-making. Designed for scalable and secure data management, Striim facilitates effective data-driven strategies.

What are some key features of Striim?

Continuous Data Ingestion: Collects and processes real-time data streams promptly.
Data Integration: Combines data from different sources for unified insights.
Real-Time Analytics: Supports instant analytics, allowing fast response to changes.
Scalable Architecture: Adapts to large-scale data environments efficiently.

What benefits should users look for in Striim reviews?

Improved Data Accuracy: Ensures data integrity in analytics.
Enhanced Operational Efficiency: Streamlined processes for faster data handling.
Cost-Effective Integration: Reduces expenses associated with traditional data handling.
Flexible Deployment Options: Allows for tailored solutions fitting specific needs.

In industries like finance, Striim supports real-time fraud detection by providing uninterrupted data streaming between transaction systems. In healthcare, it enables rapid data processing for patient monitoring, improving service delivery. Manufacturing uses Striim to enhance supply chain visibility through real-time data analytics.

Striim

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

Sky, UPS, MACY'S, EMAAR, HSBC

Find out what your peers are saying about Informatica, Microsoft, Palantir and others in Data Integration. Updated: August 2026.

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