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.
"We have found the bulk load feature very valuable."
"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."
"The most valuable feature of Azure Data Factory is the core features that help you through the whole Azure pipeline or value chain."
"It's extremely consistent."
"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."
"I enjoy the ease of use for the backend JSON generator, the deployment solution, and the template management."
"The trigger scheduling options are decently robust."
"It makes it easy to collect data from different sources."
"The most valuable feature that we use is the Knowledge Modules."
"Most of the functions are very straightforward, like the data model, mapping, package, and load plan. Thus, a new user could get started very fast."
"The Knowledge Module approach provides an easy and reusable way to create our own integration strategies. It's easy to create these Knowledge Modules to connect to new technologies, for instance."
"It allows us to use many languages to develop and to integrate practically all the technologies of the Oracle suite as well as those from non-Oracle vendors."
"The CAEM is very useful in its modularity and portability."
"ODI is a very accessible tool, especially since the mapping functionality has been added."
"It can integrate with more recent databases like Cassandra, Hadoop, and other more recent Big Data databases."
"ODI's most valuable features are it utilizes the database engine and is very lightweight."
"It's a good idea to take a Microsoft course. Because they are really helpful when you start from your journey with Data Factory."
"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."
"Sometimes I need to do some coding, and I'd like to avoid that. I'd like no-code integrations."
"The user interface could use improvement. It's not a major issue but it's something that can be improved."
"We have experienced some issues with the integration. This is an area that needs improvement."
"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."
"On the UI side, they could make it a little more intuitive in terms of how to add the radius components. Somebody who has been working with tools like Informatica or DataStage gets very used to how the UI looks and feels."
"There aren't many third-party extensions or plugins available in the solution."
"The resource management aspect of the solution could be improved."
"Technical Support could be better."
"Stability could be improved because some operators have issues."
"The solution lacks some functions and features."
"Oracle Data Integrator (ODI) is already good as a solution. Still, it needs some editing of its preview package, or if the package is upgraded, that will make Oracle Data Integrator (ODI) even better."
"ODI could improve by focusing on streamlining its features without unnecessary overhead."
"It lacks a suite of tools suitable for fully processing data and moving it into decision support warehouses."
"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 67 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, SSIS, Informatica PowerCenter, 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.
