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Azure Data Factory vs Informatica Enterprise Data Lake comparison

 

Comparison Buyer's Guide

Executive Summary

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
1st
Average Rating
8.0
Reviews Sentiment
6.9
Number of Reviews
89
Ranking in other categories
Cloud Data Warehouse (3rd)
Informatica Enterprise Data...
Ranking in Data Integration
41st
Average Rating
7.0
Reviews Sentiment
5.9
Number of Reviews
1
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of January 2025, in the Data Integration category, the mindshare of Azure Data Factory is 10.8%, down from 13.4% compared to the previous year. The mindshare of Informatica Enterprise Data Lake is 0.3%, up from 0.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Integration
 

Featured Reviews

Joy Maitra - PeerSpot reviewer
Facilitates seamless data pipeline creation with good analytics and and thorough monitoring
Azure Data Factory is a low code, no code platform, which is helpful. It provides many prebuilt functionalities that assist in building data pipelines. Also, it facilitates easy transformation with all required functionalities for analytics. Furthermore, it connects to different sources out-of-the-box, making integration much easier. The monitoring is very thorough, though a more readable version would be appreciable.
reviewer2330691 - PeerSpot reviewer
A scalable tool that needs a lot of maintenance due to its unstable nature
Governance, data dictionary, and data cataloging are not available in Informatica Enterprise Data Lake. A lot of businesses are facing issues related to understanding the area revolving around insights of data. At Informatica Enterprise Data Lake's level, in our company, we have a lot of redundant data in a lot of our core systems. The basic thing that our company wants is for the product to develop a reporting layer and access data from the document layer so that we can avoid duplication in projects, databases, and data. There is a lot of maintenance to be done owing to the instability users may face every time because of the huge processing capacity as the company has around more than 50 nodes, which causes a lot of maintenance issues because of which a lot of people don't benefit from the platform as it functions in a slow manner. Informatica Enterprise Data Lake's setup process was complex since it doesn't support a lot of real-time systems. Every time, we have to find different tools we can use in our company with the solution since it doesn't support many real-time systems. Even if our company invests in some tools, Informatica Enterprise Data Lake creates too many small files with some issues, which we cannot read because we invested in HBase and Kudu, but performance-wise, the process is slow.

Quotes from Members

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

Pros

"The overall performance is quite good."
"Azure Data Factory's most valuable features are the packages and the data transformation that it allows us to do, which is more drag and drop, or a visual interface. So, that eases the entire process."
"One advantage of Azure Data Factory is that it's fast, unlike SSIS and other on-premise tools. It's also very convenient because it has multiple connectors. The availability of native connectors allows you to connect to several resources to analyze data streams."
"It is easy to deploy workflows and schedule jobs."
"I like that it's a monolithic data platform. This is why we propose these solutions."
"The initial setup is very quick and easy."
"It's extremely consistent."
"It is easy to integrate."
"The process of using the tool's scalability option is well documented."
 

Cons

"The number of standard adaptors could be extended further."
"There should be a way that it can do switches, so if at any point in time I want to do some hybrid mode of making any data collections or ingestions, I can just click on a button."
"There is always room to improve. There should be good examples of use that, of course, customers aren't always willing to share. It is Catch-22. It would help the user base if everybody had really good examples of deployments that worked, but when you ask people to put out their good deployments, which also includes me, you usually got, "No, I'm not going to do that." They don't have enough good examples. Microsoft probably just needs to pay one of their partners to build 20 or 30 examples of functional Data Factories and then share them as a user base."
"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."
"Occasionally, there are problems within Microsoft itself that impacts the Data Factory and causes it to fail."
"Azure Data Factory can improve the transformation features. You have to do a lot of transformation activities. This is something that is just not fully covered. Additionally, the integration could improve for other tools, such as Azure Data Catalog."
"There are limitations when processing more than one GD file."
"When the record fails, it's tough to identify and log."
"Informatica Enterprise Data Lake's setup process was complex since it doesn't support a lot of real-time systems."
 

Pricing and Cost Advice

"Product is priced at the market standard."
"For our use case, it is not expensive. We take into the picture everything: resources, learning curve, and maintenance."
"Azure products generally offer competitive pricing, suitable for diverse budget considerations."
"I would rate Data Factory's pricing nine out of ten."
"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."
"The pricing is pay-as-you-go or reserve instance. Of the two options, reserve instance is much cheaper."
"ADF is cheaper compared to AWS."
"I rate the product price as six on a scale of one to ten, where one is low price and ten is high price."
"The licenses attached to the solution are highly priced."
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Top Industries

By visitors reading reviews
Financial Services Firm
13%
Computer Software Company
12%
Manufacturing Company
9%
Healthcare Company
7%
No data available
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
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.
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...
What do you like most about Informatica Enterprise Data Lake?
The process of using the tool's scalability option is well documented.
What is your experience regarding pricing and costs for Informatica Enterprise Data Lake?
The licenses attached to the solution are highly priced. Informatica has licensing models for every product and for every feature, like the web service feature, which is something my company doesn'...
What needs improvement with Informatica Enterprise Data Lake?
Governance, data dictionary, and data cataloging are not available in Informatica Enterprise Data Lake. A lot of businesses are facing issues related to understanding the area revolving around insi...
 

Also Known As

No data available
Informatica Intelligent Data Lake, Intelligent Data Lake
 

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
Information Not Available
Find out what your peers are saying about Microsoft, Informatica, Talend and others in Data Integration. Updated: January 2025.
831,265 professionals have used our research since 2012.