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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
90
Ranking in other categories
Cloud Data Warehouse (3rd)
Informatica Enterprise Data...
Ranking in Data Integration
40th
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 April 2025, in the Data Integration category, the mindshare of Azure Data Factory is 9.5%, down from 12.7% compared to the previous year. The mindshare of Informatica Enterprise Data Lake is 0.2%, 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

"From my experience so far, the best feature is the ability to copy data to any environment. We have 100 connects and we can connect them to the system and copy the data from its respective system to any environment. That is the best feature."
"The solution can scale very easily."
"Data Factory's best features are simplicity and flexibility."
"It makes it easy to collect data from different sources."
"The solution handles large volumes of data very well. One of its best features is its ability to integrate data end-to-end, from pulling data from the source to accessing Databricks. This makes it quite useful for our needs."
"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."
"Azure Data Factory became more user-friendly when data-flows were introduced."
"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."
"The process of using the tool's scalability option is well documented."
 

Cons

"When the record fails, it's tough to identify and log."
"You cannot use a custom data delimiter, which means that you have problems receiving data in certain formats."
"My only problem is the seamless connectivity with various other databases, for example, SAP."
"Currently, our company requires a monitoring tool, and that isn't available in Azure Data Factory."
"Data Factory would be improved if it were a little more configuration-oriented and not so code-oriented and if it had more automated features."
"Data Factory's cost is too high."
"Data Factory's monitorability could be better."
"There are limitations when processing more than one GD file."
"Informatica Enterprise Data Lake's setup process was complex since it doesn't support a lot of real-time systems."
 

Pricing and Cost Advice

"For our use case, it is not expensive. We take into the picture everything: resources, learning curve, and maintenance."
"While I can't specify the actual cost, I believe it is reasonably priced and comparable to similar products."
"The price is fair."
"The solution's fees are based on a pay-per-minute use plus the amount of data required to process."
"Our licensing fees are approximately 15,000 ($150 USD) per month."
"The price you pay is determined by how much you use it."
"The cost is based on the amount of data sets that we are ingesting."
"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
14%
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: March 2025.
845,406 professionals have used our research since 2012.