We performed a comparison between Apache Spark and SAP HANA based on real PeerSpot user reviews.
Find out in this report how the two Hadoop solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."The product’s most valuable features are lazy evaluation and workload distribution."
"The product's deployment phase is easy."
"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."
"The good performance. The nice graphical management console. The long list of ML algorithms."
"The features we find most valuable are the machine learning, data learning, and Spark Analytics."
"The product is useful for analytics."
"The most valuable feature of SAP HANA is its performance and integration."
"Technically it resembles Oracle, but as a somewhat lighter version."
"The performance in terms of processing time is unmatched due to the in-memory processing capability."
"Integration is the most valuable feature we use SAP HANA for."
"SAP HANA's most valuable features are monitoring, reporting, and price and stock control."
"It is very stable and very innovative. You can integrate many applications with it."
"It has a very huge bandwidth and data transfer."
"The solution is marvelous because it controls everything including workflow and that makes our company more productive."
"The solution’s integration with other platforms should be improved."
"It should support more programming languages."
"One limitation is that not all machine learning libraries and models support it."
"The initial setup was not easy."
"More ML based algorithms should be added to it, to make it algorithmic-rich for developers."
"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."
"The management tools could use improvement. Some of the debugging tools need some work as well. They need to be more descriptive."
"Stream processing needs to be developed more in Spark. I have used Flink previously. Flink is better than Spark at stream processing."
"The user interface and CRM need to be more user-friendly."
"A documents preview could be helpful."
"The initial setup was pretty complex, considering the enormous amount of data they had from an Oracle ERP."
"When you do a report on a non-SAP platform, you face some compatibility problems."
"The performance and integration with other products are areas in need of improvement."
"This is an expensive solution."
"I would like to see improvements in the connectivity of the solution with other BI software. Not every software can connect to it natively."
"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."
Apache Spark is ranked 1st in Hadoop with 60 reviews while SAP HANA is ranked 1st in Embedded Database with 81 reviews. Apache Spark is rated 8.4, while SAP HANA is rated 8.4. 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 SAP HANA writes "Excellent compatibility between modules and the control". Apache Spark is most compared with Spring Boot, AWS Batch, Spark SQL, Cloudera Distribution for Hadoop and Azure Stream Analytics, whereas SAP HANA is most compared with Oracle Database, SQL Server, MySQL, IBM Db2 Database and SAP Adaptive Server Enterprise. See our Apache Spark vs. SAP HANA report.
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