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."When we grade big jobs from on-prem to the cloud, we do it in EMR with Spark."
"The initial setup is straightforward."
"The initial setup is pretty straightforward."
"The solution helps us manage huge volumes of data."
"Amazon EMR is a good solution that can be used to manage big data."
"The solution is pretty simple to set up."
"The ability to resize the cluster is what really makes it stand out over other Hadoop and big data solutions."
"In Amazon EMR it is easy to rebuild anything, easy to upgrade and has good fault tolerance."
"The stability was fine. It behaved as expected."
"Spark SQL's efficiency in managing distributed data and its simplicity in expressing complex operations make it an essential part of our data pipeline."
"The team members don't have to learn a new language and can implement complex tasks very easily using only SQL."
"Overall the solution is excellent."
"This solution is useful to leverage within a distributed ecosystem."
"Certain data sets that are very large are very difficult to process with Pandas and Python libraries. Spark SQL has helped us a lot with that."
"The solution is easy to understand if you have basic knowledge of SQL commands."
"Data validation and ease of use are the most valuable features."
"The most complicated thing is configuring to the cluster and ensure it's running correctly."
"The initial setup was time-consuming."
"There is room for improvement in pricing."
"Modules and strategies should be better handled and notified early in advance."
"The legacy versions of the solution are not supported in the new versions."
"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 problem for us is it starts very slow."
"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."
"It would be beneficial for aggregate functions to include a code block or toolbox that explains its calculations or supported conditional statements."
"There are many inconsistencies in syntax for the different querying tasks."
"In the next release, maybe the visualization of some command-line features could be added."
"In terms of improvement, the only thing that could be enhanced is the stability aspect of Spark SQL."
"It takes a bit of time to get used to using this solution versus Pandas as it has a steep learning curve."
"This solution could be improved by adding monitoring and integration for the EMR."
"There should be better integration with other solutions."
Amazon EMR is ranked 3rd in Hadoop with 20 reviews while Spark SQL is ranked 4th in Hadoop with 14 reviews. Amazon EMR is rated 7.8, while Spark SQL is rated 7.8. The top reviewer of Amazon EMR writes "Provides efficient data processing features and has good scalability ". On the other hand, the top reviewer of Spark SQL writes "Offers the flexibility to handle large-scale data processing". Amazon EMR is most compared with Snowflake, Cloudera Distribution for Hadoop, Azure Data Factory, Amazon Redshift and Apache Spark, whereas Spark SQL is most compared with Apache Spark, IBM Db2 Big SQL, HPE Ezmeral Data Fabric, SAP HANA and Netezza Analytics. See our Amazon EMR vs. Spark SQL report.
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