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."Features include machine learning, real time streaming, and data processing."
"With Spark, we parallelize our operations, efficiently accessing both historical and real-time data."
"It is highly scalable, allowing you to efficiently work with extensive datasets that might be problematic to handle using traditional tools that are memory-constrained."
"ETL and streaming capabilities."
"The most crucial feature for us is the streaming capability. It serves as a fundamental aspect that allows us to exert control over our operations."
"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 provides a scalable machine learning library."
"I like that it can handle multiple tasks parallelly. I also like the automation feature. JavaScript also helps with the parallel streaming of the library."
"It is a stable solution...It is a scalable solution."
"The solution is easy to scale."
"The solution is very stable."
"It is very flexible to integrate with SaaS components."
"SAP HANA's interface is pretty user-friendly."
"It has a very huge bandwidth and data transfer."
"The main feature is that the processes are very flexible, they are able to be adapted to the business and their departments."
"The solution operates well."
"The initial setup was not easy."
"The logging for the observability platform could be better."
"There were some problems related to the product's compatibility with a few Python libraries."
"They could improve the issues related to programming language for the platform."
"Stability in terms of API (things were difficult, when transitioning from RDD to DataFrames, then to DataSet)."
"The solution needs to optimize shuffling between workers."
"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."
"When you are working with large, complex tasks, the garbage collection process is slow and affects performance."
"Per SAP, you can do both transactional and analytical processes in SAP HANA. Though that's true, the speed is slower when you combine the two functions, so this is what I'd like SAP to improve in SAP HANA. In the next release, I want to see better diagrams in SAP HANA and a more user-friendly interface."
"It could be a bit more scalable."
"I would like to see improvements in the connectivity of the solution with other BI software. Not every software can connect to it natively."
"The product is very demanding on memory requirements."
"More standards would help in the future."
"It is challenging to integrate it with third-party tools."
"The JDBC connectors are very slow."
"The user experience should be better. Its user interface is not good. I also don't like the transition concept."
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 AWS Lambda, 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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