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."I feel the streaming is its best feature."
"The most crucial feature for us is the streaming capability. It serves as a fundamental aspect that allows us to exert control over our operations."
"Spark can handle small to huge data and is suitable for any size of company."
"Its scalability and speed are very valuable. You can scale it a lot. It is a great technology for big data. It is definitely better than a lot of earlier warehouse or pipeline solutions, such as Informatica. Spark SQL is very compliant with normal SQL that we have been using over the years. This makes it easy to code in Spark. It is just like using normal SQL. You can use the APIs of Spark or you can directly write SQL code and run it. This is something that I feel is useful in Spark."
"Features include machine learning, real time streaming, and data processing."
"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."
"ETL and streaming capabilities."
"The data processing framework is good."
"It has the best proxy, security, and support features compared to open-source products."
"In terms of scalability, if you have enough hardware you can scale out. Scalability doesn't have any issues."
"The product provides better data processing features than other tools."
"Very good end-to-end security features."
"Customer service and support were able to fix whatever the issue was."
"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."
"The product is completely secure."
"The most valuable feature is Kubernetes."
"The solution’s integration with other platforms should be improved."
"I know there is always discussion about which language to write applications in and some people do love Scala. However, I don't like it."
"One limitation is that not all machine learning libraries and models support it."
"We've had problems using a Python process to try to access something in a large volume of data. It crashes if somebody gives me the wrong code because it cannot handle a large volume of data."
"There could be enhancements in optimization techniques, as there are some limitations in this area that could be addressed to further refine Spark's performance."
"Apache Spark is very difficult to use. It would require a data engineer. It is not available for every engineer today because they need to understand the different concepts of Spark, which is very, very difficult and it is not easy to learn."
"Apache Spark's GUI and scalability could be improved."
"It should support more programming languages."
"The price of this solution could be lowered."
"There are better solutions out there that have more features than this one."
"Cloudera Distribution for Hadoop is not always completely stable in some cases, which can be a concern for big data solutions."
"The areas of improvement depend on the scale of the project. For banking customers, security features and an essential budget for commercial licenses would be the top priority. Data regulation could be the most crucial for a project with extensive data or an extra use case."
"The solution does not support multiple languages very well and this means users need to create work-arounds to implement some solutions."
"While the deployed product is generally functional, there are instances where it presents difficulties."
"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."
"The dashboard could be improved."
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Apache Spark is ranked 2nd in Hadoop with 58 reviews while Cloudera Distribution for Hadoop is ranked 1st in Hadoop with 46 reviews. Apache Spark is rated 8.4, while Cloudera Distribution for Hadoop is rated 7.8. 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, Cassandra, MongoDB and InfluxDB. See our Apache Spark vs. Cloudera Distribution for Hadoop report.
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