We performed a comparison between Amazon EMR and Apache Spark 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 solution helps us manage huge volumes of data."
"The ability to resize the cluster is what really makes it stand out over other Hadoop and big data solutions."
"The initial setup is pretty straightforward."
"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."
"The solution is pretty simple to set up."
"Amazon EMR's most valuable features are processing speed and data storage capacity."
"Amazon EMR is a good solution that can be used to manage big data."
"The project management is very streamlined."
"The product's deployment phase is easy."
"DataFrame: Spark SQL gives the leverage to create applications more easily and with less coding effort."
"The data processing framework is good."
"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."
"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."
"The features we find most valuable are the machine learning, data learning, and Spark Analytics."
"The product’s most valuable features are lazy evaluation and workload distribution."
"The dashboard management could be better. Right now, it's lacking a bit."
"There were times where they would release new versions and it seemed to end up breaking old versions, which is very strange."
"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 legacy versions of the solution are not supported in the new versions."
"The problem for us is it starts very slow."
"Amazon EMR is continuously improving, but maybe something like CI/CD out-of-the-box or integration with Prometheus Grafana."
"The product's features for storing data in static clusters could be better."
"In data analysis, you need to take real-time data from different data sources. You need to process this in a subsecond, do the transformation in a subsecond, and all that."
"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."
"When using Spark, users may need to write their own parallelization logic, which requires additional effort and expertise."
"Its UI can be better. Maintaining the history server is a little cumbersome, and it should be improved. I had issues while looking at the historical tags, which sometimes created problems. You have to separately create a history server and run it. Such things can be made easier. Instead of separately installing the history server, it can be made a part of the whole setup so that whenever you set it up, it becomes available."
"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 solution must improve its performance."
"At the initial stage, the product provides no container logs to check the activity."
"When you first start using this solution, it is common to run into memory errors when you are dealing with large amounts of data."
Amazon EMR is ranked 3rd in Hadoop with 20 reviews while Apache Spark is ranked 1st in Hadoop with 60 reviews. Amazon EMR is rated 7.8, while Apache Spark is rated 8.4. The top reviewer of Amazon EMR writes "Provides efficient data processing features and has good scalability ". On the other hand, the top reviewer of Apache Spark writes "Reliable, able to expand, and handle large amounts of data well". Amazon EMR is most compared with Snowflake, Cloudera Distribution for Hadoop, Azure Data Factory, Amazon Redshift and Microsoft Azure Synapse Analytics, whereas Apache Spark is most compared with Spring Boot, AWS Batch, Spark SQL, SAP HANA and Vert.x. See our Amazon EMR vs. Apache Spark report.
See our list of best Hadoop vendors.
We monitor all Hadoop reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.
