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Ab Initio Co>Operating System vs Azure Data Factory comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Dec 19, 2024
 

Categories and Ranking

Ab Initio Co>Operating System
Ranking in Data Integration
47th
Average Rating
9.6
Reviews Sentiment
7.9
Number of Reviews
2
Ranking in other categories
Workload Automation (28th)
Azure Data Factory
Ranking in Data Integration
1st
Average Rating
8.0
Reviews Sentiment
6.9
Number of Reviews
86
Ranking in other categories
Cloud Data Warehouse (3rd)
 

Mindshare comparison

As of December 2024, in the Data Integration category, the mindshare of Ab Initio Co>Operating System is 1.3%, up from 0.6% compared to the previous year. The mindshare of Azure Data Factory is 11.0%, down from 13.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Integration
 

Featured Reviews

RV
Excellent bulk data processing for large enterprises
Co>Operating System's most valuable feature is its ability to process bulk data effectively Co>Operating System would be improved with more integrations for less well-known technologies. In the next release, Co>Operating System should include some AI capabilities on the data governance side, like…
Thulani David Mngadi - PeerSpot reviewer
Data flow feature is valuable for data transformation tasks
The workflow automation features in GitLab, particularly its low code/no code approach, are highly beneficial for accelerating development speed. This feature allows for quick creation of pipelines and offers customization options for integration needs, making it versatile for various use cases. GitLab supports a wide range of connectors, catering to a majority of integration needs. Azure Data Factory's virtual enterprise and monitoring capabilities, the visual interface of GitLab makes it user-friendly and easy to teach, facilitating adoption within teams. While the monitoring capabilities are sufficient out of the box, they may not be as comprehensive as dedicated enterprise monitoring tools. GitLab's monitoring features are manageable for production use, with the option to integrate log analytics or create custom dashboards if needed. The data flow feature in Azure Data Factory within GitLab is valuable for data transformation tasks, especially for those who may not have expertise in writing complex code. It simplifies the process of data manipulation and is particularly useful for individuals unfamiliar with Spark coding. While there could be improvements for more flexibility, overall, the data flow feature effectively accomplishes its purpose within GitLab's ecosystem.

Quotes from Members

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

Pros

"Co>Operating System's most valuable feature is its ability to process bulk data effectively."
"Ab Initio reaches the highest performance and is very flexible in processing huge amounts of data."
"For me, it was that there are dedicated connectors for different targets or sources, different data sources. For example, there is direct connector to Salesforce, Oracle Service Cloud, etcetera, and that was really helpful."
"The best part of this product is the extraction, transformation, and load."
"It is a complete ETL Solution."
"Data Factory allows you to pull data from multiple systems, transform it according to your business needs, and load it into a data warehouse or data lake."
"It's cloud-based, allowing multiple users to easily access the solution from the office or remote locations. I like that we can set up the security protocols for IP addresses, like allow lists. It's a pretty user-friendly product as well. The interface and build environment where you create pipelines are easy to use. It's straightforward to manage the digital transformation pipelines we build."
"We use the solution to move data from on-premises to the cloud."
"Data Factory's best features are connectivity with different tools and focusing data ingestion using pipeline copy data."
"In terms of my personal experience, it works fine."
 

Cons

"An awesome improvement would be big data solutions, for example, implementing some kind of business intelligence or neural networks for artificial intelligence."
"Co>Operating System would be improved with more integrations for less well-known technologies."
"The performance could be better. It would be better if Azure Data Factory could handle a higher load. I have heard that it can get overloaded, and it can't handle it."
"Sometimes I need to do some coding, and I'd like to avoid that. I'd like no-code integrations."
"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."
"Azure Data Factory uses many resources and has issues with parallel workflows."
"There is no built-in pipeline exit activity when encountering an error."
"Customer service is not satisfactory. Third-party personnel handle support and rely on a knowledge repository."
"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."
"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."
 

Pricing and Cost Advice

"Co>Operating System's pricing is on the expensive end since it tends to be used by big enterprises."
"In terms of licensing costs, we pay somewhere around S14,000 USD per month. There are some additional costs. For example, we would have to subscribe to some additional computing and for elasticity, but they are minimal."
"Product is priced at the market standard."
"It seems very low initially, but as the data grows, the solution’s bills grow exponentially."
"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."
"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."
"Data Factory is expensive."
"It's not particularly expensive."
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Top Industries

By visitors reading reviews
Financial Services Firm
38%
Computer Software Company
9%
Insurance Company
8%
University
5%
Financial Services Firm
13%
Computer Software Company
12%
Manufacturing Company
9%
Healthcare Company
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
 

Questions from the Community

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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...
 

Also Known As

Co>Operating System
No data available
 

Learn More

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Overview

 

Sample Customers

A multinational transportation company
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
Find out what your peers are saying about Ab Initio Co>Operating System vs. Azure Data Factory and other solutions. Updated: December 2024.
824,053 professionals have used our research since 2012.