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."The most valuable feature is the Fault Tolerance and easy binding with other processes like Machine Learning, graph analytics."
"The most valuable feature of Apache Spark is its ease of use."
"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 is highly scalable, allowing you to efficiently work with extensive datasets that might be problematic to handle using traditional tools that are memory-constrained."
"Provides a lot of good documentation compared to other solutions."
"The good performance. The nice graphical management console. The long list of ML algorithms."
"I found the solution stable. We haven't had any problems with it."
"The solution has been very stable."
"The upgrades and patches must come from Hortonworks."
"It is a scalable platform."
"Hortonworks should not be expensive at all to those looking into using it."
"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."
"The data platform is pretty neat. The workflow is also really good."
"Ranger for security; with Ranger we can manager user’s permissions/access controls very easily."
"We use it for data science activities."
"The scalability is the key reason why we are on this platform."
"Apache Spark should add some resource management improvements to the algorithms."
"This solution currently cannot support or distribute neural network related models, or deep learning related algorithms. We would like this functionality to be developed."
"It should support more programming languages."
"Its UI can be better. Maintaining the history server is a little cumbersome, and it should be improved. I had issues while looking at the historical tags, which sometimes created problems. You have to separately create a history server and run it. Such things can be made easier. Instead of separately installing the history server, it can be made a part of the whole setup so that whenever you set it up, it becomes available."
"One limitation is that not all machine learning libraries and models support it."
"It would be beneficial to enhance Spark's capabilities by incorporating models that utilize features not traditionally present in its framework."
"I would like to see integration with data science platforms to optimize the processing capability for these tasks."
"The solution needs to optimize shuffling between workers."
"Hive performance. If Hive performance increased, Hadoop would replace (not everywhere) traditional databases."
"The cost of the solution is high and there is room for improvement."
"It's at end of life and no longer will there be improvements."
"More information could be there to simplify the process of running the product."
"I work a lot with banking, IT and communications customers. Hortonworks must improve or must upgrade their services for these sectors."
"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."
"It would also be nice if there were less coding involved."
"Security and workload management need improvement."
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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