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."Spark can handle small to huge data and is suitable for any size of company."
"The distribution of tasks, like the seamless map-reduce functionality, is quite impressive."
"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."
"Apache Spark provides a very high-quality implementation of distributed data processing."
"The processing time is very much improved over the data warehouse solution that we were using."
"Now, when we're tackling sentiment analysis using NLP technologies, we deal with unstructured data—customer chats, feedback on promotions or demos, and even media like images, audio, and video files. For processing such data, we rely on PySpark. Beneath the surface, Spark functions as a compute engine with in-memory processing capabilities, enhancing performance through features like broadcasting and caching. It's become a crucial tool, widely adopted by 90% of companies for a decade or more."
"The product's deployment phase is easy."
"The features we find most valuable are the machine learning, data learning, and Spark Analytics."
"Ambari Web UI: user-friendly."
"The scalability is the key reason why we are on this platform."
"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."
"It is a scalable platform."
"Ranger for security; with Ranger we can manager user’s permissions/access controls very easily."
"The upgrades and patches must come from Hortonworks."
"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."
"We use it for data science activities."
"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."
"When you first start using this solution, it is common to run into memory errors when you are dealing with large amounts of data."
"The initial setup was not easy."
"The solution must improve its performance."
"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."
"The solution needs to optimize shuffling between workers."
"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."
"The product could improve the user interface and make it easier for new users."
"I would like to see more support for containers such as Docker and OpenShift."
"The version control of the software is also an issue."
"I work a lot with banking, IT and communications customers. Hortonworks must improve or must upgrade their services for these sectors."
"The cost of the solution is high and there is room for improvement."
"It would also be nice if there were less coding involved."
"More information could be there to simplify the process of running the product."
"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."
"Deleting any service requires a lot of clean up, unlike Cloudera."
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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