We performed a comparison between Cloudera Distribution for Hadoop 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."We experienced many issues when we started working with Hadoop 3.0 in the Cloudera 6.0 version, so there are a lot of things that need to improve. I believe they are working on that."
"The product provides better data processing features than other tools."
"The features I find most valuable is that the solution is that it is easy to install and to work with. It starts with the installation and from there on the management is very simple and centralized."
"Provides a viable open-source solution for enterprise implementations and reliable, intelligent data analysis."
"We also really like the Cloudera community. You can have any question and will have your answer within a few hours."
"It has the best proxy, security, and support features compared to open-source products."
"The product is completely secure."
"Very good end-to-end security features."
"It is a stable solution."
"The stability was fine. It behaved as expected."
"Overall the solution is excellent."
"The performance is one of the most important features. It has an API to process the data in a functional manner."
"This solution is useful to leverage within a distributed ecosystem."
"One of Spark SQL's most beautiful features is running parallel queries to go through enormous data."
"Data validation and ease of use are the most valuable features."
"The speed of getting data."
"Cloudera Distribution for Hadoop is not always completely stable in some cases, which can be a concern for big data solutions."
"The procedure for operations could be simplified."
"While the deployed product is generally functional, there are instances where it presents difficulties."
"It could be faster and more user-friendly."
"The pricing needs to improve."
"It would be useful if Cloudera had more tools like SQL Engines that offer the traditional relational database. We have to do a lot of work preparing the data outside Cloudera before getting it into the platform."
"This is a very expensive solution."
"The tool's ability to be deployed on a cloud model is an area of concern where improvements are required."
"There should be better integration with other solutions."
"I've experienced some incompatibilities when using the Delta Lake format."
"In terms of improvement, the only thing that could be enhanced is the stability aspect of Spark SQL."
"It takes a bit of time to get used to using this solution versus Pandas as it has a steep learning curve."
"There are many inconsistencies in syntax for the different querying tasks."
"It would be useful if Spark SQL integrated with some data visualization tools."
"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."
More Cloudera Distribution for Hadoop Pricing and Cost Advice →
Cloudera Distribution for Hadoop is ranked 2nd in Hadoop with 47 reviews while Spark SQL is ranked 4th in Hadoop with 14 reviews. Cloudera Distribution for Hadoop is rated 8.0, while Spark SQL is rated 7.8. The top reviewer of Cloudera Distribution for Hadoop writes "Good end-to-end security features and we like that it's cloud independent". On the other hand, the top reviewer of Spark SQL writes "Offers the flexibility to handle large-scale data processing". Cloudera Distribution for Hadoop is most compared with Amazon EMR, HPE Ezmeral Data Fabric, Apache Spark, MongoDB and Cassandra, whereas Spark SQL is most compared with Apache Spark, IBM Db2 Big SQL, SAP HANA, HPE Ezmeral Data Fabric and Netezza Analytics. See our Cloudera Distribution for Hadoop vs. Spark SQL report.
See our list of best Hadoop vendors.
We monitor all Hadoop reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.
