We performed a comparison between Azure Data Factory and Informatica Powercenter based on our users’ reviews in four categories. After reading all of the collected data, you can find our conclusion below.
Comparison Results: Based on the parameters we compared, Azure Data Factory and Informatica Powercenter are very comparable to one another. Overall, PeerSpot users found that both solutions have helpful features. However, users found Informatica Powercenter to be more expensive than Azure Data Factory.
"Data Factory's best feature is the ease of setting up pipelines for data and cloud integrations."
"We use the solution to move data from on-premises to the cloud."
"Data Factory's most valuable feature is Copy Activity."
"The best part of this product is the extraction, transformation, and load."
"The most valuable feature is the ease in which you can create an ETL pipeline."
"It is very modular. It works well. We've used Data Factory and then made calls to libraries outside of Data Factory to do things that it wasn't optimized to do, and it worked really well. It is obviously proprietary in regards to Microsoft created it, but it is pretty easy and direct to bring in outside capabilities into Data Factory."
"Data Factory's best features are simplicity and flexibility."
"The most valuable features of the solution are its ease of use and the readily available adapters for connecting with various sources."
"The interface is very clean and clear."
"To me, what's most valuable in Informatica PowerCenter is the flexibility in building the integration pipeline. Usually, you need to have a platform to be able to integrate with different technologies, including legacy data such as the mainframe. The platform should also be rich enough to transform the data per your business requirement, with no restrictions. Rich integration and rich transformation capabilities are the two key capabilities in Informatica PowerCenter. The solution also offers ease of use. Another valuable feature of Informatica PowerCenter is the drag-and-drop integration because it's GUI-based, similar to IBM and Oracle."
"We can scale the product."
"Informatica PowerCenter has been implementing mapping design, data flow, and workflow execution for years."
"We use Informatica PowerCenter to transfer the transitional database to and from the data warehouse. This is very efficient as it enables us to quickly find our data reports and the data, so we can build AI models."
"The greatest feature is that it is very easy to have someone come in and jump right in. It is one of the nicest tools in terms of getting a person acquainted quickly."
"Has a good visual tool for data mapping."
"It has very good monitoring and process monitoring."
"When working with AWS, we have noticed that the difference between ADF and AWS is that AWS is more customer-focused. They're more responsive compared to any other company. ADF is not as good as AWS, but it should be. If AWS is ten out of ten, ADF is around eight out of ten. I think AWS is easier to understand from the GUI perspective compared to ADF."
"Some known bugs and issues with Azure Data Factory could be rectified."
"The number of standard adaptors could be extended further."
"The user interface could use improvement. It's not a major issue but it's something that can be improved."
"Data Factory has so many features that it can be a little difficult or confusing to find some settings and configurations. I'm sure there's a way to make it a little easier to navigate."
"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."
"The need to work more on developing out-of-the-box connectors for other products like Oracle, AWS, and others."
"There's no Oracle connector if you want to do transformation using data flow activity, so Azure Data Factory needs more connectors for data flow transformation."
"Unstructured data handling is an important area with a shortcoming that needs improvement in the solution."
"It should be more cloud-centric than on-prem-centric."
"As a connector to big data, it is not well developed. We've had problems connecting Informatica with Hadoop. The functionality to connect Informatica with Hadoop, for me it's not good."
"Informatica, in my opinion, is very rigid and not very flexible, whereas platforms like Alteryx or Matillion are very flexible and agile."
"There is some room for improvement in terms of pricing."
"The price of the product is an area of concern where improvements are required, considering the fact that the present licensing charges of the tool are expensive."
"If we could have the option of performance improvement within Informatica, and if it could have more features, that would be ideal."
"While Informatica is great for data-integration, it does not have any analytics features. Thus, organizations have to always look for another product for their BI needs."
Azure Data Factory is ranked 1st in Data Integration with 81 reviews while Informatica PowerCenter is ranked 3rd in Data Integration with 78 reviews. Azure Data Factory is rated 8.0, while Informatica PowerCenter is rated 8.0. The top reviewer of Azure Data Factory writes "The data factory agent is quite good but pricing needs to be more transparent". On the other hand, the top reviewer of Informatica PowerCenter writes "Stable, provides good support, and integrating it with other systems is very fast, but its pricing is expensive". Azure Data Factory is most compared with Informatica Cloud Data Integration, Alteryx Designer, Snowflake, Microsoft Azure Synapse Analytics and IBM InfoSphere DataStage, whereas Informatica PowerCenter is most compared with Informatica Cloud Data Integration, SSIS, Databricks, AWS Glue and Oracle Data Integrator (ODI). See our Azure Data Factory vs. Informatica PowerCenter report.
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We monitor all Data Integration reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.
