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."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."
"When we grade big jobs from on-prem to the cloud, we do it in EMR with Spark."
"Amazon EMR is a good solution that can be used to manage big data."
"The initial setup is straightforward."
"This is the best tool for hosts and it's really flexible and scalable."
"The solution is scalable."
"The initial setup is pretty straightforward."
"The project management is very streamlined."
"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 performance is one of the most important features. It has an API to process the data in a functional manner."
"It is a stable solution."
"Overall the solution is excellent."
"The solution is easy to understand if you have basic knowledge of SQL commands."
"The speed of getting data."
"I find the Thrift connection valuable."
"Offers a variety of methods to design queries and incorporates the regular SQL syntax within tasks."
"There is room for improvement in pricing."
"The problem for us is it starts very slow."
"There were times where they would release new versions and it seemed to end up breaking old versions, which is very strange."
"Modules and strategies should be better handled and notified early in advance."
"The initial setup was time-consuming."
"The dashboard management could be better. Right now, it's lacking a bit."
"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."
"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."
"It takes a bit of time to get used to using this solution versus Pandas as it has a steep learning curve."
"In terms of improvement, the only thing that could be enhanced is the stability aspect of Spark SQL."
"It would be useful if Spark SQL integrated with some data visualization tools."
"SparkUI could have more advanced versions of the performance and the queries and all."
"I've experienced some incompatibilities when using the Delta Lake format."
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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