We performed a comparison between Azure Data Factory and Oracle Data Integrator ODI based on our users’ reviews in five categories. After reading all of the collected data, you can find our conclusion below.
Comparison Results: The main difference between the two products is that Azure Data Factory needs better integration capabilities while users of Oracle Data Integrator (ODI) find that the solution integrates well with other systems.
"The most valuable features are data transformations."
"The initial setup is very quick and easy."
"The function of the solution is great."
"The data mapping and the ability to systematically derive data are nice features. It worked really well for the solution we had. It is visual, and it did the transformation as we wanted."
"In terms of my personal experience, it works fine."
"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."
"We haven't had any issues connecting it to other products."
"Powerful but easy-to-use and intuitive."
"The scalability is great. It's one of the reasons we chose the solution."
"The installation of the client ODI Studio is easy."
"It's scalable."
"All ETL code is stored in repositories in underlying database schemas. The number of users can access and work on the same solution using a client tool. So distributed teams can work on this tool in an efficient manner."
"I like that Oracle Data Integrator (ODI) has a straightforward setup and offers good technical support."
"It's completely user-friendly."
"It can integrate with more recent databases like Cassandra, Hadoop, and other more recent Big Data databases."
"ODI is a very accessible tool, especially since the mapping functionality has been added."
"The product could provide more ways to import and export data."
"The need to work more on developing out-of-the-box connectors for other products like Oracle, AWS, and others."
"The user interface could use improvement. It's not a major issue but it's something that can be improved."
"Areas for improvement in Azure Data Factory include connectivity and integration. When you use integration runtime, whenever there's a failure, the backup process in Azure Data Factory takes time, so this is another area for improvement."
"Azure Data Factory can improve by having support in the drivers for change data capture."
"Sometimes I need to do some coding, and I'd like to avoid that. I'd like no-code integrations."
"A room for improvement in Azure Data Factory is its speed. Parallelization also needs improvement."
"We are too early into the entire cycle for us to really comment on what problems we face. We're mostly using it for transformations, like ETL tasks. I think we are comfortable with the facts or the facts setting. But for other parts, it is too early to comment on."
"There are certain things where it can be improved. Initial solution setup seems a bit complex at the start, it should be improved because it becomes bit tough for a novice to get started on this. Sometimes error description is not helpful to understand the problem it gives some generic type of errors which are at times not that helpful to understand the underlying root cause of the issue."
"The price needs to be lowered. It's too expensive."
"The performance of the user interface is in need of improvement."
"The initial setup is a bit complex compared to other tools."
"It lacks a suite of tools suitable for fully processing data and moving it into decision support warehouses."
"It needs easier security."
"At present, when multiple steps are executed in parallel in the load plan and errors occur, the error handling mechanism does not function correctly."
"The interface of ODI could be improved. For example, navigating and finding functions can be difficult. For example, you have to know which step you need to go to look at where your job status is. The logical step is a bit complex compared to other tools. It's much easier to get a graphical view, but with ODI, it's graphical, plus you have to know all the other pieces that fit around it. You have to think about the logical and physical aspects."
Azure Data Factory is ranked 1st in Data Integration with 81 reviews while Oracle Data Integrator (ODI) is ranked 4th in Data Integration with 68 reviews. Azure Data Factory is rated 8.0, while Oracle Data Integrator (ODI) is rated 8.2. 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 Oracle Data Integrator (ODI) writes "Straightforward to implement, scalable, and has good stability and documentation, but technical support could still be improved". Azure Data Factory is most compared with Informatica PowerCenter, Informatica Cloud Data Integration, Alteryx Designer, Snowflake and SAP Data Services, whereas Oracle Data Integrator (ODI) is most compared with Oracle Integration Cloud Service, Informatica PowerCenter, SSIS, Oracle GoldenGate and Talend Open Studio. See our Azure Data Factory vs. Oracle Data Integrator (ODI) 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.
