We performed a comparison between Apache Spark and Oracle Application Development Framework based on real PeerSpot user reviews.Find out in this report how the two Java Frameworks solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.
"The most valuable feature of Apache Spark is its ease of use."
"One of the key features is that Apache Spark is a distributed computing framework. You can help multiple slaves and distribute the workload between them."
"It is useful for handling large amounts of data. It is very useful for scientific purposes."
"There's a lot of functionality."
"Spark helps us reduce startup time for our customers and gives a very high ROI in the medium term."
"Its scalability and speed are very valuable. You can scale it a lot. It is a great technology for big data. It is definitely better than a lot of earlier warehouse or pipeline solutions, such as Informatica. Spark SQL is very compliant with normal SQL that we have been using over the years. This makes it easy to code in Spark. It is just like using normal SQL. You can use the APIs of Spark or you can directly write SQL code and run it. This is something that I feel is useful in Spark."
"One of Apache Spark's most valuable features is that it supports in-memory processing, the execution of jobs compared to traditional tools is very fast."
"We can create objects that allow us to develop pages and applications very rapidly."
"The best part of Oracle ADF is being able to easily write code in Java with JavaBean files."
"It's database-centric, and it's seemingly easy to use the model–view–controller pattern that's built-in."
"The single sign-on features applied to Oracle Cloud is a valuable feature. All parts of this application are compatible with single sign-on, where you have a security feature that is very good in Oracle Cloud."
"The most valuable feature of the Oracle Application Development Framework is the rapid development and the security it provides."
"Its UI can be better. Maintaining the history server is a little cumbersome, and it should be improved. I had issues while looking at the historical tags, which sometimes created problems. You have to separately create a history server and run it. Such things can be made easier. Instead of separately installing the history server, it can be made a part of the whole setup so that whenever you set it up, it becomes available."
"Apache Spark could improve the connectors that it supports. There are a lot of open-source databases in the market. For example, cloud databases, such as Redshift, Snowflake, and Synapse. Apache Spark should have connectors present to connect to these databases. There are a lot of workarounds required to connect to those databases, but it should have inbuilt connectors."
"Apache Spark can improve the use case scenarios from the website. There is not any information on how you can use the solution across the relational databases toward multiple databases."
"I know there is always discussion about which language to write applications in and some people do love Scala. However, I don't like it."
"The initial setup was not easy."
"This solution currently cannot support or distribute neural network related models, or deep learning related algorithms. We would like this functionality to be developed."
"The logging for the observability platform could be better."
"When you are working with large, complex tasks, the garbage collection process is slow and affects performance."
"You need to have Oracle ADF on-premises to build a big project. You need to have a dependable front-end application."
"The model layer could be improved for performance because once that part gets bloated, the performance is lacking. So, there is room for performance optimization."
"The UI is very slow and not up to market standard."
"Oracle Application Development Framework is set to go out of support over the next three years but they should provide support for the solution for the longer term. Additionally, there needs to be more overall optimization and specifically in webpage rendering. The solution uses a lot of resources, and in order for them to move forward, they would have to create a smaller resource impact."
"I use JDeveloper along with ADF and, unfortunately, JDeveloper is a very slow tool. It takes a lot of time to accomplish things with it during both development and deployment. I hope that Oracle will improve JDeveloper to make it run faster."
Spark provides programmers with an application programming interface centered on a data structure called the resilient distributed dataset (RDD), a read-only multiset of data items distributed over a cluster of machines, that is maintained in a fault-tolerant way. It was developed in response to limitations in the MapReduce cluster computing paradigm, which forces a particular linear dataflowstructure on distributed programs: MapReduce programs read input data from disk, map a function across the data, reduce the results of the map, and store reduction results on disk. Spark's RDDs function as a working set for distributed programs that offers a (deliberately) restricted form of distributed shared memory
Oracle ADF is an end-to-end Java EE framework that simplifies application development by providing out-of-the-box infrastructure services and a visual and declarative development experience. Oracle ADF simplifies Java EE development by minimizing the need to write code that implements the applicationâs infrastructure allowing the developers to focus on the features of the actual application. Oracle ADF provides these infrastructure implementations as part of the framework. It also implements the Model-View-Controller design pattern and offers an integrated solution that covers all the layers of the architecture integrated with the Oracle SOA and WebCenter Portal frameworks.
Apache Spark is ranked 2nd in Java Frameworks with 12 reviews while Oracle Application Development Framework is ranked 5th in Java Frameworks with 5 reviews. Apache Spark is rated 8.0, while Oracle Application Development Framework is rated 7.6. The top reviewer of Apache Spark writes "Reliable, able to expand, and handle large amounts of data well". On the other hand, the top reviewer of Oracle Application Development Framework writes "Eases the writing of code in Java with JavaBeans; easy to set up". Apache Spark is most compared with Spring Boot, Azure Stream Analytics, AWS Batch, AWS Lambda and Hortonworks Data Platform, whereas Oracle Application Development Framework is most compared with Spring Boot, Spring MVC, Jakarta EE and Amazon Corretto. See our Apache Spark vs. Oracle Application Development Framework report.
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