We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
"The most valuable feature of this solution is its capacity for processing large amounts of data."
"The solution is very stable."
"I feel the streaming is its best feature."
"The features we find most valuable are the machine learning, data learning, and Spark Analytics."
"The main feature that we find valuable is that it is very fast."
"The processing time is very much improved over the data warehouse solution that we were using."
"The memory processing engine is the solution's most valuable aspect. It processes everything extremely fast, and it's in the cluster itself. It acts as a memory engine and is very effective in processing data correctly."
"AI libraries are the most valuable. They provide extensibility and usability. Spark has a lot of connectors, which is a very important and useful feature for AI. You need to connect a lot of points for AI, and you have to get data from those systems. Connectors are very wide in Spark. With a Spark cluster, you can get fast results, especially for AI."
"The most valuable features of this solution are the business components."
"There are several valuable features. First is the fast deployment. Also the ease of use."
"The most valuable feature is the ease of integration with other Oracle products."
"The power of Oracle ADF is in the business components."
"When you first start using this solution, it is common to run into memory errors when you are dealing with large amounts of data."
"The solution needs to optimize shuffling between workers."
"When you want to extract data from your HDFS and other sources then it is kind of tricky because you have to connect with those sources."
"We've had problems using a Python process to try to access something in a large volume of data. It crashes if somebody gives me the wrong code because it cannot handle a large volume of data."
"We use big data manager but we cannot use it as conditional data so whenever we're trying to fetch the data, it takes a bit of time."
"I would like to see integration with data science platforms to optimize the processing capability for these tasks."
"The graphical user interface (UI) could be a bit more clear. It's very hard to figure out the execution logs and understand how long it takes to send everything. If an execution is lost, it's not so easy to understand why or where it went. I have to manually drill down on the data processes which takes a lot of time. Maybe there could be like a metrics monitor, or maybe the whole log analysis could be improved to make it easier to understand and navigate."
"Stream processing needs to be developed more in Spark. I have used Flink previously. Flink is better than Spark at stream processing."
"The performance of this solution needs to be improved because it is very slow."
"Lacks tailoring to geographic regional differences and consistent integration with third parties."
"The application needs to be more lightweight and the performance improved."
"Oracle ADF needs more components and the layout can be improved."
"Apache Spark is open-source. You have to pay only when you use any bundled product, such as Cloudera."
"The cost of this solution is approximately $47,000 USD per site."
"We use a lot of Oracle products and in total, we pay about £5 million ($6.1 million USD) per year."
Earn 20 points
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 1st in Java Frameworks with 11 reviews while Oracle Application Development Framework is ranked 4th in Java Frameworks with 4 reviews. Apache Spark is rated 8.6, while Oracle Application Development Framework is rated 7.8. The top reviewer of Apache Spark writes "Good Streaming features enable to enter data and analysis within Spark Stream". On the other hand, the top reviewer of Oracle Application Development Framework writes "Easy to teach to programmers especially regarding how to capture the technology and how to enhance it". Apache Spark is most compared with Spring Boot, Azure Stream Analytics, AWS Batch, AWS Lambda and SAP HANA, whereas Oracle Application Development Framework is most compared with Spring Boot and Spring MVC. See our Apache Spark vs. Oracle Application Development Framework report.
See our list of best Java Frameworks vendors.
We monitor all Java Frameworks 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.