"The processing time is very much improved over the data warehouse solution that we were using."
"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 solution has been very stable."
"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."
"Apache Spark can do large volume interactive data analysis."
"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."
"Using this solution has given us better details for reporting and analytics."
"The in-memory computing and the efficient response time are very good features."
"All features are valuable."
"We've had good experiences with technical support."
"This is a feature-rich product and I like all of them."
"The performance in terms of processing time is unmatched due to the in-memory processing capability."
"The solution operates well."
"The solution is extremely stable. That's the most important aspect of the solution, for our organization. There is no downtime, and the performance is very good."
"The logging for the observability platform could be better."
"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."
"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."
"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."
"I would like to see integration with data science platforms to optimize the processing capability for these tasks."
"Stream processing needs to be developed more in Spark. I have used Flink previously. Flink is better than Spark at stream processing."
"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."
"Apache Spark is very difficult to use. It would require a data engineer. It is not available for every engineer today because they need to understand the different concepts of Spark, which is very, very difficult and it is not easy to learn."
"Uses a large amount of RAM and is costly."
"It would be nice to know when SAP plans to stop its maintenance of a previous version of SAP ECC ERP because, at this point, anyone utilizing SAP will have no choice but to go on S/4HANA Database."
"I'd just like to see some more improvements done on the training, both on the functional training and technical training sides as a part of the complete solution."
"The user interface and CRM need to be more user-friendly."
"The product is very demanding on memory requirements."
"The worst thing about SAP HANA is the price; it's very expensive. The licensing cost, implementation cost, hosting cost, and appliance cost are all high."
"The documentation can be improved in the future."
"SAP HANA is a very proprietary tool and there's not as much support available for it as there is for an SQL Server (which is more popular)."
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
The SAP HANA® platform helps you reimagine business by combining a robust database with services for creating innovative applications. It enables real-time business by converging trans-actions and analytics on one in-memory platform. Running on premise or in the cloud, SAP HANA untangles IT complexity, bringing huge savings in data management and empowering decision makers everywhere with new insight and predictive power.
Apache Spark is ranked 1st in Hadoop with 9 reviews while SAP HANA is ranked 1st in Embedded Database Software with 25 reviews. Apache Spark is rated 8.4, while SAP HANA is rated 8.0. The top reviewer of Apache Spark writes "Provides fast aggregations, AI libraries, and a lot of connectors". On the other hand, the top reviewer of SAP HANA writes "Very robust solution with good data access". Apache Spark is most compared with Spring Boot, Azure Stream Analytics, AWS Lambda, AWS Batch and Cloudera Distribution for Hadoop, whereas SAP HANA is most compared with SQL Server, Oracle Database, MySQL, IBM Db2 Database and Oracle Database In-Memory.
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