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."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."
"It's easy to prepare parallelism in Spark, run the solution with specific parameters, and get good performance."
"The features we find most valuable are the machine learning, data learning, and Spark Analytics."
"DataFrame: Spark SQL gives the leverage to create applications more easily and with less coding effort."
"Spark can handle small to huge data and is suitable for any size of company."
"The most valuable feature of Apache Spark is its ease of use."
"I appreciate everything about the solution, not just one or two specific features. The solution is highly stable. I rate it a perfect ten. The solution is highly scalable. I rate it a perfect ten. The initial setup was straightforward. I recommend using the solution. Overall, I rate the solution a perfect ten."
"We use it for ETL purposes as well as for implementing the full transformation pipelines."
"The solution operates well."
"The main feature is that the processes are very flexible, they are able to be adapted to the business and their departments."
"It is very flexible to integrate with SaaS components."
"It has a very huge bandwidth and data transfer."
"The most valuable features I have found are speed, dashboard, and reporting."
"The in-memory computing and the efficient response time are very good features."
"In comparison with other DMS solutions, it offers good performance."
"It's sufficed all of our requirements. We primarily needed it to run SAP applications, like NetWeaver or S/4HANA, and it has been really good at that."
"I would like to see integration with data science platforms to optimize the processing capability for these tasks."
"Needs to provide an internal schedule to schedule spark jobs with monitoring capability."
"The logging for the observability platform could be better."
"Technical expertise from an engineer is required to deploy and run high-tech tools, like Informatica, on Apache Spark, making it an area where improvements are required to make the process easier for users."
"Apache Spark can improve the use case scenarios from the website. There is not any information on how you can use the solution across the relational databases toward multiple databases."
"We are building our own queries on Spark, and it can be improved in terms of query handling."
"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."
"Apache Spark should add some resource management improvements to the algorithms."
"I would like to see improvement on the feedback from the road-map; it is currently extremely hard to get insight in this area."
"The inclusion of a well-performing Time Machine is vital."
"The surface side or Attack dashboard needs improvement because there are some gaps after sales services."
"In my limited experience using SAP, the process of granting access to different modules is difficult. Specifically, the requirement to assign roles and key codes to users rather than being able to assign them individually made the process more complex. It would be beneficial if there was a way to assign key codes separately, rather than having to create multiple roles. This would make managing access easier."
"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."
"I give the scalability of SAP HANA a six out of ten."
"In terms of improvement, the speed is not as good as we thought it would be. That is why we are trying different solutions that will be built with different technologies."
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.
See our list of best Hadoop vendors.
We monitor all Hadoop reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.