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 ability to resize the cluster is what really makes it stand out over other Hadoop and big data solutions."
"The solution is scalable."
"The solution helps us manage huge volumes of data."
"The project management is very streamlined."
"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."
"It has a variety of options and support systems."
"The initial setup is pretty straightforward."
"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."
"This solution is useful to leverage within a distributed ecosystem."
"The performance is one of the most important features. It has an API to process the data in a functional manner."
"The speed of getting data."
"One of Spark SQL's most beautiful features is running parallel queries to go through enormous data."
"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 solution is easy to understand if you have basic knowledge of SQL commands."
"Overall the solution is excellent."
"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."
"The most complicated thing is configuring to the cluster and ensure it's running correctly."
"Amazon EMR is continuously improving, but maybe something like CI/CD out-of-the-box or integration with Prometheus Grafana."
"There is room for improvement in pricing."
"The legacy versions of the solution are not supported in the new versions."
"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 product's features for storing data in static clusters could be better."
"In terms of improvement, the only thing that could be enhanced is the stability aspect of Spark SQL."
"In the next release, maybe the visualization of some command-line features could be added."
"It would be beneficial for aggregate functions to include a code block or toolbox that explains its calculations or supported conditional statements."
"It takes a bit of time to get used to using this solution versus Pandas as it has a steep learning curve."
"Anything to improve the GUI would be helpful."
"SparkUI could have more advanced versions of the performance and the queries and all."
"There should be better integration with other solutions."
"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."
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, SAP HANA, HPE Ezmeral Data Fabric and Netezza Analytics. See our Amazon EMR vs. Spark SQL report.
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