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."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."
"With Hadoop-related technologies, we can distribute the workload with multiple commodity hardware."
"It provides a scalable machine learning library."
"With Spark, we parallelize our operations, efficiently accessing both historical and real-time data."
"We use Spark to process data from different data sources."
"The good performance. The nice graphical management console. The long list of ML algorithms."
"The most valuable feature of Apache Spark is its ease of use."
"The most valuable feature of Apache Spark is its flexibility."
"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."
"Cloudera is a very manageable solution with good support."
"The search function is the most valuable aspect of the solution."
"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 file system is a valuable feature."
"The solution is reliable and stable, it fits our requirements."
"It has the best proxy, security, and support features compared to open-source products."
"The product as a whole is good."
"When you want to extract data from your HDFS and other sources then it is kind of tricky because you have to connect with those sources."
"The migration of data between different versions could be improved."
"It would be beneficial to enhance Spark's capabilities by incorporating models that utilize features not traditionally present in its framework."
"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."
"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."
"The logging for the observability platform could be better."
"When you are working with large, complex tasks, the garbage collection process is slow and affects performance."
"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."
"Cloudera Distribution for Hadoop has a limited feature list and a lot of costs involved."
"There is a maximum of a one-gigabyte block size, which is an area of storage that can be improved upon."
"The dashboard could be improved."
"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 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 Cloudera training has deteriorated significantly."
"Cloudera Distribution for Hadoop is not always completely stable in some cases, which can be a concern for big data solutions."
"This is a very expensive solution."
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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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