We performed a comparison between Amazon EMR, Apache Spark, and Cloudera Distribution for Hadoop based on real PeerSpot user reviews.
Find out what your peers are saying about Apache, Cloudera, Amazon Web Services (AWS) and others in Hadoop."The project management is very streamlined."
"We are using applications, such as Splunk, Livy, Hadoop, and Spark. We are using all of these applications in Amazon EMR and they're helping us a lot."
"The initial setup is pretty straightforward."
"The solution is pretty simple to set up."
"It allows users to access the data through a web interface."
"One of the valuable features about this solution is that it's managed services, so it's pretty stable, and scalable as much as you wish. It has all the necessary distributions. With some additional work, it's also possible to change to a Spark version with the latest version of EMR. It also has Hudi, so we are leveraging Apache Hudi on EMR for change data capture, so then it comes out-of-the-box in EMR."
"In Amazon EMR it is easy to rebuild anything, easy to upgrade and has good fault tolerance."
"The ability to resize the cluster is what really makes it stand out over other Hadoop and big data solutions."
"ETL and streaming capabilities."
"The deployment of the product is easy."
"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."
"There's a lot of functionality."
"I found the solution stable. We haven't had any problems with it."
"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."
"It is highly scalable, allowing you to efficiently work with extensive datasets that might be problematic to handle using traditional tools that are memory-constrained."
"The scalability of Cloudera Distribution for Hadoop is excellent."
"I don't see any performance issues."
"We had a data warehouse before all the data. We can process a lot more data structures."
"It has the best proxy, security, and support features compared to open-source products."
"The product provides better data processing features than other tools."
"The solution is stable."
"The tool can be deployed using different container technologies, which makes it very scalable."
"The product as a whole is good."
"We don't have much control. If we have multiple users, if they want to scale up, the cost will go and increase and we don't know how we can restrict that price part."
"The product must add some of the latest technologies to provide more flexibility to the users."
"The most complicated thing is configuring to the cluster and ensure it's running correctly."
"The problem for us is it starts very slow."
"Amazon EMR can improve by adding some features, such as megastore services and HiveServer2. Additionally, the user interface could be better, similar to what Apache service provides, cross-platform services."
"Modules and strategies should be better handled and notified early in advance."
"There is no need to pay extra for third-party software."
"There is room for improvement in pricing."
"The solution needs to optimize shuffling between workers."
"The setup I worked on was really complex."
"I would like to see integration with data science platforms to optimize the processing capability for these tasks."
"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."
"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."
"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."
"We are building our own queries on Spark, and it can be improved in terms of query handling."
"Include more machine learning algorithms and the ability to handle streaming of data versus micro batch processing."
"The Cloudera training has deteriorated significantly."
"The user infrastructure and user interface needs to be improved, as well as the performance. The GUI needs to be better."
"The initial setup of Cloudera is difficult."
"There are multiple bugs when we update."
"The solution is not fit for on-premise distributions."
"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 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."
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