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."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."
"One of Apache Spark's most valuable features is that it supports in-memory processing, the execution of jobs compared to traditional tools is very fast."
"AI libraries are the most valuable. They provide extensibility and usability. Spark has a lot of connectors, which is a very important and useful feature for AI. You need to connect a lot of points for AI, and you have to get data from those systems. Connectors are very wide in Spark. With a Spark cluster, you can get fast results, especially for AI."
"The product’s most valuable feature is the SQL tool. It enables us to create a database and publish it."
"It's easy to prepare parallelism in Spark, run the solution with specific parameters, and get good performance."
"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."
"The tool's most valuable feature is its speed and efficiency. It's much faster than other tools and excels in parallel data processing. Unlike tools like Python or JavaScript, which may struggle with parallel processing, it allows us to handle large volumes of data with more power easily."
"The solution is very stable."
"It is helpful to gather and process data."
"The product is completely secure."
"In terms of scalability, if you have enough hardware you can scale out. Scalability doesn't have any issues."
"The tool can be deployed using different container technologies, which makes it very scalable."
"The most valuable feature is that I can use CDH for almost all use cases across all industries, including the financial sector, public sector, private retailers, and so on."
"The solution is reliable and stable, it fits our requirements."
"The most valuable feature is Kubernetes."
"CDH has a wide variety of proprietary tools that we use, like Impala. So from that perspective, it's quite useful as opposed to something open-source. We get a lot of value from Cloudera's proprietary tools."
"The solution needs to optimize shuffling between workers."
"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."
"They could improve the issues related to programming language for the platform."
"When you first start using this solution, it is common to run into memory errors when you are dealing with large amounts of data."
"We are building our own queries on Spark, and it can be improved in terms of query handling."
"Dynamic DataFrame options are not yet available."
"It should support more programming languages."
"It needs a new interface and a better way to get some data. In terms of writing our scripts, some processes could be faster."
"The user infrastructure and user interface needs to be improved, as well as the performance. The GUI needs to be better."
"Cloudera Distribution for Hadoop is not always completely stable in some cases, which can be a concern for big data solutions."
"The tool's ability to be deployed on a cloud model is an area of concern where improvements are required."
"The initial setup of Cloudera is difficult."
"They should focus on upgrading their technical capabilities in the market."
"The solution does not support multiple languages very well and this means users need to create work-arounds to implement some solutions."
"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."
"Cloudera Distribution for Hadoop has a limited feature list and a lot of costs involved."
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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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