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."One of the key features is that Apache Spark is a distributed computing framework. You can help multiple slaves and distribute the workload between them."
"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 most valuable features are lazy evaluation and workload distribution."
"Features include machine learning, real time streaming, and data processing."
"The distribution of tasks, like the seamless map-reduce functionality, is quite impressive."
"The product is useful for analytics."
"DataFrame: Spark SQL gives the leverage to create applications more easily and with less coding effort."
"With Spark, we parallelize our operations, efficiently accessing both historical and real-time data."
"Customer service and support were able to fix whatever the issue was."
"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."
"In terms of scalability, if you have enough hardware you can scale out. Scalability doesn't have any issues."
"The scalability of Cloudera Distribution for Hadoop is excellent."
"With a cluster available, you can manage the security layer using the shared SDX - it provides flexibility."
"The most valuable feature is Kubernetes."
"We had a data warehouse before all the data. We can process a lot more data structures."
"Cloudera is a very manageable solution with good support."
"Include more machine learning algorithms and the ability to handle streaming of data versus micro batch processing."
"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."
"Apache Spark could potentially improve in terms of user-friendliness, particularly for individuals with a SQL background. While it's suitable for those with programming knowledge, making it more accessible to those without extensive programming skills could be beneficial."
"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 initial setup was not easy."
"Apache Spark provides very good performance The tuning phase is still tricky."
"Apache Spark can improve the use case scenarios from the website. There is not any information on how you can use the solution across the relational databases toward multiple databases."
"At the initial stage, the product provides no container logs to check the activity."
"The procedure for operations could be simplified."
"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."
"The dashboard could be improved."
"Cloudera Distribution for Hadoop is not always completely stable in some cases, which can be a concern for big data solutions."
"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 security of this solution could be improved. There should also be a way to basically have a blockchain enabled storage with the HDFS."
"The governance aspect of the solution should be improved."
"There are better solutions out there that have more features than this one."
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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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