We performed a comparison between Hortonworks Data Platform and Spark SQL 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."Ranger for security; with Ranger we can manager user’s permissions/access controls very easily."
"We use it for data science activities."
"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."
"Ambari Web UI: user-friendly."
"Hortonworks should not be expensive at all to those looking into using it."
"Distributed computing, secure containerization, and governance capabilities are the most valuable features."
"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."
"It is a scalable platform."
"It is a stable solution."
"The performance is one of the most important features. It has an API to process the data in a functional manner."
"The speed of getting data."
"This solution is useful to leverage within a distributed ecosystem."
"The solution is easy to understand if you have basic knowledge of SQL commands."
"Certain data sets that are very large are very difficult to process with Pandas and Python libraries. Spark SQL has helped us a lot with that."
"Data validation and ease of use are the most valuable features."
"One of Spark SQL's most beautiful features is running parallel queries to go through enormous data."
"It's at end of life and no longer will there be improvements."
"Deleting any service requires a lot of clean up, unlike Cloudera."
"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."
"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."
"More information could be there to simplify the process of running the product."
"I would like to see more support for containers such as Docker and OpenShift."
"Being a new user, I am not able to find out how to partition it correctly. I probably need more information or knowledge. In other database solutions, you can easily optimize all partitions. I haven't found a quicker way to do that in Spark SQL. It would be good if you don't need a partition here, and the system automatically partitions in the best way. They can also provide more educational resources for new users."
"I've experienced some incompatibilities when using the Delta Lake format."
"Anything to improve the GUI would be helpful."
"In the next update, we'd like to see better performance for small points of data. It is possible but there are better tools that are faster and cheaper."
"There are many inconsistencies in syntax for the different querying tasks."
"There should be better integration with other solutions."
"In the next release, maybe the visualization of some command-line features could be added."
"The solution needs to include graphing capabilities. Including financial charts would help improve everything overall."
Hortonworks Data Platform is ranked 6th in Hadoop with 25 reviews while Spark SQL is ranked 4th in Hadoop with 14 reviews. Hortonworks Data Platform is rated 8.0, while Spark SQL is rated 7.8. The top reviewer of Hortonworks Data Platform writes "Good for secure containerization, and governance capabilities ". On the other hand, the top reviewer of Spark SQL writes "Offers the flexibility to handle large-scale data processing". Hortonworks Data Platform is most compared with Amazon EMR, Apache Spark, Cloudera DataFlow and HPE Ezmeral Data Fabric, whereas Spark SQL is most compared with Apache Spark, IBM Db2 Big SQL, SAP HANA, HPE Ezmeral Data Fabric and Netezza Analytics. See our Hortonworks Data Platform vs. Spark SQL report.
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