We performed a comparison between Apache Spark and Hortonworks Data Platform 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."We use Spark to process data from different data sources."
"This solution provides a clear and convenient syntax for our analytical tasks."
"The most valuable feature of Apache Spark is its memory processing because it processes data over RAM rather than disk, which is much more efficient and fast."
"AI libraries are the most valuable. They provide extensibility and usability. Spark has a lot of connectors, which is a very important and useful feature for AI. You need to connect a lot of points for AI, and you have to get data from those systems. Connectors are very wide in Spark. With a Spark cluster, you can get fast results, especially for AI."
"The solution is very stable."
"DataFrame: Spark SQL gives the leverage to create applications more easily and with less coding effort."
"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."
"I found the solution stable. We haven't had any problems with it."
"The data platform is pretty neat. The workflow is also really good."
"The scalability is the key reason why we are on this platform."
"Now, using this solution, it is much cheaper to have all of the data available for searching, not in real-time, but whenever there is a pending request."
"The Hortonworks solution is so stable. It is working as a production system, without any error, without any downtime. If I have downtime, it is mostly caused by the hardware of the computers."
"We use it for data science activities."
"The product offers a fairly easy setup process."
"Ranger for security; with Ranger we can manager user’s permissions/access controls very easily."
"Distributed computing, secure containerization, and governance capabilities are the most valuable features."
"There could be enhancements in optimization techniques, as there are some limitations in this area that could be addressed to further refine Spark's performance."
"If you have a Spark session in the background, sometimes it's very hard to kill these sessions because of D allocation."
"Stream processing needs to be developed more in Spark. I have used Flink previously. Flink is better than Spark at stream processing."
"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."
"At times during the deployment process, the tool goes down, making it look less robust. To take care of the issues in the deployment process, users need to do manual interventions occasionally."
"Spark could be improved by adding support for other open-source storage layers than Delta Lake."
"At the initial stage, the product provides no container logs to check the activity."
"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."
"Security and workload management need improvement."
"I work a lot with banking, IT and communications customers. Hortonworks must improve or must upgrade their services for these sectors."
"Hive performance. If Hive performance increased, Hadoop would replace (not everywhere) traditional databases."
"Deleting any service requires a lot of clean up, unlike Cloudera."
"The cost of the solution is high and there is room for improvement."
"Since Cloudera acquired HDP, it's been bundled with CBH and HDP. However, the biggest challenge is cloud storage integration with Azure, GCP, and AWS."
"I would like to see more support for containers such as Docker and OpenShift."
"More information could be there to simplify the process of running the product."
Apache Spark is ranked 1st in Hadoop with 60 reviews while Hortonworks Data Platform is ranked 6th in Hadoop with 25 reviews. Apache Spark is rated 8.4, while Hortonworks Data Platform is rated 8.0. 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 Hortonworks Data Platform writes "Good for secure containerization, and governance capabilities ". Apache Spark is most compared with Spring Boot, AWS Batch, Spark SQL, SAP HANA and Cloudera Distribution for Hadoop, whereas Hortonworks Data Platform is most compared with Amazon EMR, Cloudera DataFlow and HPE Ezmeral Data Fabric. See our Apache Spark vs. Hortonworks Data Platform report.
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