We performed a comparison between Azure Data Factory and Oracle Autonomous Data Warehouse based on real PeerSpot user reviews.
Find out in this report how the two Cloud Data Warehouse solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."The most valuable features of the solution are its ease of use and the readily available adapters for connecting with various sources."
"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."
"We have been using drivers to connect to various data sets and consume data."
"Powerful but easy-to-use and intuitive."
"Data Factory's best feature is the ease of setting up pipelines for data and cloud integrations."
"It is easy to integrate."
"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 am one hundred percent happy with the stability."
"It is a stable and scalable solution."
"I loved the simplicity of loading the data and simply relying on the self-tuning capabilities of ADW."
"A very good integration feature that restricts access to unauthorized people."
"The analytics have been very good. We've found them to be quite useful."
"The solution is self-securing. All data is encrypted and security updates and patches are applied automatically both periodically and off-cycle."
"It is a very stable tool...It is an extremely scalable tool."
"It provides Transparent Data Encryption (TDE) capabilities by default to address data security issues."
"With Oracle Autonomous Data Warehouse, things are much simpler. Creating a structure, initializing the servers, extending the servers, those are all things that are very, very easy. That's the main reason we use it."
"I would like to be informed about the changes ahead of time, so we are aware of what's coming."
"One area for improvement is documentation. At present, there isn't enough documentation on how to use Azure Data Factory in certain conditions. It would be good to have documentation on the various use cases."
"For some of the data, there were some issues with data mapping. Some of the error messages were a little bit foggy. There could be more of a quick start guide or some inline examples. The documentation could be better."
"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."
"The product could provide more ways to import and export data."
"When the record fails, it's tough to identify and log."
"Data Factory's monitorability could be better."
"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."
"A lot of the tools that were previously there have now been taken away."
"Ease of connectivity could be improved."
"Sometimes the solution works differently between the cloud and on-premises. It needs to be more consistent and predictable."
"There is a need for more storage to be allocated, but over a period of time, it becomes impossible to reduce it after using it."
"My main suggestion for Oracle is the configuration and key values that come for JSON files. When we create a table, especially if you see in our RedShift or some other stuff, if I create a table on top of a JSON file with multiple array columns or superset columns, those column values create some difficulty in Oracle."
"The installation process is complex. Oracle can make the installation process better."
"They should make the solution more user-friendly."
"It is very important the integration with other platforms be made to be as easy as it is with an on-premises deployment."
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Azure Data Factory is ranked 3rd in Cloud Data Warehouse with 81 reviews while Oracle Autonomous Data Warehouse is ranked 10th in Cloud Data Warehouse with 16 reviews. Azure Data Factory is rated 8.0, while Oracle Autonomous Data Warehouse is rated 8.6. 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 Autonomous Data Warehouse writes "A tool for data warehousing that offers scalability, stability, and ease of setup". Azure Data Factory is most compared with Informatica PowerCenter, Informatica Cloud Data Integration, Alteryx Designer, Snowflake and IBM InfoSphere DataStage, whereas Oracle Autonomous Data Warehouse is most compared with Oracle Exadata, Snowflake, Microsoft Azure Synapse Analytics, BigQuery and Vertica. See our Azure Data Factory vs. Oracle Autonomous Data Warehouse report.
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