We performed a comparison between Apache Spark and Cloudera Distribution for Hadoop 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 helps us reduce startup time for our customers and gives a very high ROI in the medium term."
"The distribution of tasks, like the seamless map-reduce functionality, is quite impressive."
"The good performance. The nice graphical management console. The long list of ML algorithms."
"The product is useful for analytics."
"The data processing framework is good."
"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."
"The solution has been very stable."
"With Hadoop-related technologies, we can distribute the workload with multiple commodity hardware."
"The tool can be deployed using different container technologies, which makes it very scalable."
"We're now able to store large volumes of data through Cloudera Distribution for Hadoop. We're able to push large volumes of data to the platform, and that used to be a challenge, especially when storing a terabyte of information. This is the area where Cloudera Distribution for Hadoop improved the organization."
"We had a data warehouse before all the data. We can process a lot more data structures."
"The solution is stable."
"The file system is a valuable feature."
"The data science aspect of the solution is valuable."
"Very good end-to-end security features."
"The product provides better data processing features than other tools."
"Apart from the restrictions that come with its in-memory implementation. It has been improved significantly up to version 3.0, which is currently in use."
"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."
"Needs to provide an internal schedule to schedule spark jobs with monitoring capability."
"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."
"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."
"We are building our own queries on Spark, and it can be improved in terms of query handling."
"The solution needs to optimize shuffling between workers."
"Spark could be improved by adding support for other open-source storage layers than Delta Lake."
"There are multiple bugs when we update."
"Without the big data environment, we cannot store all of this data live. We have billions of records and terabytes of storage to be used. It's not an option actually for us to have a big data environment."
"They should focus on upgrading their technical capabilities in the market."
"The one thing that we struggled with predominately was support. Because it was relatively new, support was always a big issue and I think it's still a bit of an ongoing concern with the team currently managing it."
"The dashboard could be improved."
"This is a very expensive solution."
"I would like to see an improvement in how the solution helps me to handle the whole cluster."
"We experienced many issues when we started working with Hadoop 3.0 in the Cloudera 6.0 version, so there is a lot of things that need to improve."
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Apache Spark is ranked 1st in Hadoop with 60 reviews while Cloudera Distribution for Hadoop is ranked 2nd in Hadoop with 47 reviews. Apache Spark is rated 8.4, while Cloudera Distribution for Hadoop 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 Cloudera Distribution for Hadoop writes "Good end-to-end security features and we like that it's cloud independent". Apache Spark is most compared with Spring Boot, AWS Batch, Spark SQL, SAP HANA and AWS Lambda, whereas Cloudera Distribution for Hadoop is most compared with Amazon EMR, HPE Ezmeral Data Fabric, MongoDB, Cassandra and InfluxDB. See our Apache Spark vs. Cloudera Distribution for Hadoop report.
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