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."Provides a lot of good documentation compared to other solutions."
"The good performance. The nice graphical management console. The long list of ML algorithms."
"The data processing framework is good."
"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 most valuable feature of Apache Spark is its flexibility."
"The most valuable feature of Apache Spark is its ease of use."
"Apache Spark provides a very high-quality implementation of distributed data processing."
"Apache Spark can do large volume interactive data analysis."
"The solution can easily be modeled."
"The feature I found most valuable in SAP HANA is modeling. I also like that the solution has good integration and you can integrate it with any system, even third-party systems."
"I like the integration process. Also, the data is trusted by our management, and we use the data from transactions for analysis."
"Eases management of databases."
"The initial setup is straightforward. It usually takes around eight months but it depends on a customer's requirements. We can spend a month or two customizing."
"The performance is very, very good. It's one of the best aspects of the solution."
"The speed at which it gets the data is great."
"It is very flexible to integrate with SaaS components."
"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."
"The management tools could use improvement. Some of the debugging tools need some work as well. They need to be more descriptive."
"The setup I worked on was really complex."
"They could improve the issues related to programming language for the platform."
"The solution needs to optimize shuffling between workers."
"In data analysis, you need to take real-time data from different data sources. You need to process this in a subsecond, do the transformation in a subsecond, and all that."
"One limitation is that not all machine learning libraries and models support it."
"It requires overcoming a significant learning curve due to its robust and feature-rich nature."
"SAP HANA is not perfect and they could improve by having more options and more integration."
"The SAP HANA interface has room for improvement because it takes more work to manage than the Microsoft SQL Server interface."
"The performance and integration with other products are areas in need of improvement."
"More standards would help in the future."
"The openness of the system could be more developed. The solution should go into the cloud. The cloud mechanism should be more invested."
"It is challenging to integrate it with third-party tools."
"They should develop and improve the solution's data management system."
"I give the scalability of SAP HANA a six out of ten."
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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