We performed a comparison between Amazon EMR and Spark SQL 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 is scalable."
"Amazon EMR is a good solution that can be used to manage big data."
"The initial setup is pretty straightforward."
"It has a variety of options and support systems."
"It allows users to access the data through a web interface."
"In Amazon EMR it is easy to rebuild anything, easy to upgrade and has good fault tolerance."
"When we grade big jobs from on-prem to the cloud, we do it in EMR with Spark."
"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 speed of getting data."
"Overall the solution is excellent."
"The stability was fine. It behaved as expected."
"This solution is useful to leverage within a distributed ecosystem."
"Data validation and ease of use are the most valuable features."
"Spark SQL's efficiency in managing distributed data and its simplicity in expressing complex operations make it an essential part of our data pipeline."
"I find the Thrift connection valuable."
"It is a stable solution."
"As people are shifting from legacy solutions to other technologies, Amazon EMR needs to add more features that give more flexibility in managing user data."
"The product must add some of the latest technologies to provide more flexibility to the users."
"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."
"Amazon EMR is continuously improving, but maybe something like CI/CD out-of-the-box or integration with Prometheus Grafana."
"There were times where they would release new versions and it seemed to end up breaking old versions, which is very strange."
"The dashboard management could be better. Right now, it's lacking a bit."
"The initial setup was time-consuming."
"Modules and strategies should be better handled and notified early in advance."
"It takes a bit of time to get used to using this solution versus Pandas as it has a steep learning curve."
"Being a new user, I am not able to find out how to partition it correctly. I probably need more information or knowledge. In other database solutions, you can easily optimize all partitions. I haven't found a quicker way to do that in Spark SQL. It would be good if you don't need a partition here, and the system automatically partitions in the best way. They can also provide more educational resources for new users."
"Anything to improve the GUI would be helpful."
"This solution could be improved by adding monitoring and integration for the EMR."
"In the next update, we'd like to see better performance for small points of data. It is possible but there are better tools that are faster and cheaper."
"There are many inconsistencies in syntax for the different querying tasks."
"SparkUI could have more advanced versions of the performance and the queries and all."
"It would be useful if Spark SQL integrated with some data visualization tools."
Amazon EMR is ranked 3rd in Hadoop with 12 reviews while Spark SQL is ranked 4th in Hadoop with 7 reviews. Amazon EMR is rated 7.8, while Spark SQL is rated 7.8. The top reviewer of Amazon EMR writes "User-friendly, easy to deploy, and good fault tolerance". On the other hand, the top reviewer of Spark SQL writes "Processing solution used for data engineering and transformation with the ability to process large datasets". Amazon EMR is most compared with Cloudera Distribution for Hadoop, Snowflake, Amazon Redshift, Azure Data Factory and Apache Spark, whereas Spark SQL is most compared with Apache Spark, IBM Db2 Big SQL, SAP HANA, HPE Ezmeral Data Fabric and Netezza Analytics. See our Amazon EMR vs. Spark SQL report.
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