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 most valuable feature of this solution is its capacity for processing large amounts of data."
"The scalability has been the most valuable aspect of the solution."
"The distribution of tasks, like the seamless map-reduce functionality, is quite impressive."
"Features include machine learning, real time streaming, and data processing."
"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."
"We use Spark to process data from different data sources."
"The product is useful for analytics."
"The processing time is very much improved over the data warehouse solution that we were using."
"We can save data very easily."
"If you want to scale with new processes and new reports, that's fairly easy."
"Integration is the most valuable feature we use SAP HANA for."
"It's easy to use, and the Hana Studio is pretty good."
"The solution is very stable."
"SAP HANA is a stable solution."
"Technically it resembles Oracle, but as a somewhat lighter version."
"It's stable and reliable."
"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."
"Stability in terms of API (things were difficult, when transitioning from RDD to DataFrames, then to DataSet)."
"The logging for the observability platform could be better."
"Apart from the restrictions that come with its in-memory implementation. It has been improved significantly up to version 3.0, which is currently in use."
"One limitation is that not all machine learning libraries and models support it."
"We've had problems using a Python process to try to access something in a large volume of data. It crashes if somebody gives me the wrong code because it cannot handle a large volume of data."
"There were some problems related to the product's compatibility with a few Python libraries."
"Stream processing needs to be developed more in Spark. I have used Flink previously. Flink is better than Spark at stream processing."
"The openness of the system could be more developed. The solution should go into the cloud. The cloud mechanism should be more invested."
"I would like more technical documentation. I would like it to be easier to find online help or have a better launch-based service. SAP has a lot of functions, so we need more best practices and more detailed documentation on industry solutions. For example, it would be good to have documentation on why a certain process needs to be set up and which kinds of configurations need to be set up."
"There are a few areas wherein there could be a patch upgrade, and that can cover up the country-specific payroll areas."
"There's an issue in the partition. When you record more than two million records, partitioning does not work well. In Oracle it's easy. SAP must resolve this issue in order to be more competitive with Oracle."
"The solution's development platform should be more flexible and scalable to adapt to other solutions."
"The SAP ERP processes are quite complex and it can be challenging."
"The product lacks some flexibility in its settings and configurations."
"The product is very demanding on memory requirements."
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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