"The solution has been very stable."
"The features we find most valuable are the machine learning, data learning, and Spark Analytics."
"Apache Spark can do large volume interactive data analysis."
"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."
"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."
"The main feature that we find valuable is that it is very fast."
"We have found the solution to be customizable and it is beneficial it comes as a bundled package. Additionally, it is user-friendly."
"The performance is very, very good. It's one of the best aspects of the solution."
"Eases management of databases."
"We've had good experiences with technical support."
"All features are valuable."
"The solution is easy to manage enterprise resources and the reporting and analytics are including. It is good for company growth and all module are managed well."
"It is difficult for me to narrow down what the best features are in SAP HANA because they work together to provide the overall functionality of the solution. However, the Fiori application is very good."
"It's easy to use, and the Hana Studio is pretty good."
"Stream processing needs to be developed more in Spark. I have used Flink previously. Flink is better than Spark at stream processing."
"The logging for the observability platform could be better."
"I would like to see integration with data science platforms to optimize the processing capability for these tasks."
"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."
"It's not easy to install."
"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."
"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."
"A documents preview could be helpful."
"The documentation can be improved in the future."
"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 user interface and CRM need to be more user-friendly."
"Technical support could be better."
"High availability and disaster recovery are very poor in HANA."
"They can improve their technology for the CRM subsystem. There are other products that are better and more effective for the CRM subsystem. Its price could be better. It is expensive."
"Technical support should be more customer-friendly."
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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