"The GUI interface is nice and easy to use."
"It is a stable solution."
"Data validation and ease of use are the most valuable features."
"The team members don't have to learn a new language and can implement complex tasks very easily using only SQL."
"One of Spark SQL's most beautiful features is running parallel queries to go through enormous data."
"Offers a variety of methods to design queries and incorporates the regular SQL syntax within tasks."
"Overall the solution is excellent."
"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 stability was fine. It behaved as expected."
"There was an issue with the incremental aggregation not working as indicated."
"The organization of the icons is not saved across users."
"The product was not able to meet our 10 second refresh requirements."
"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."
"This solution could be improved by adding monitoring and integration for the EMR."
"In terms of improvement, the only thing that could be enhanced is the stability aspect of Spark SQL."
"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 useful if Spark SQL integrated with some data visualization tools."
"It takes a bit of time to get used to using this solution versus Pandas as it has a steep learning curve."
"The solution needs to include graphing capabilities. Including financial charts would help improve everything overall."
"SparkUI could have more advanced versions of the performance and the queries and all."
Earn 20 points
AtScale Adaptive Analytics (A3) is ranked 5th in Data Virtualization while Spark SQL is ranked 4th in Hadoop with 14 reviews. AtScale Adaptive Analytics (A3) is rated 5.0, while Spark SQL is rated 7.8. The top reviewer of AtScale Adaptive Analytics (A3) writes "The GUI interface is nice and easy to use, but the organization of the icons is not saved across users". On the other hand, the top reviewer of Spark SQL writes "Offers the flexibility to handle large-scale data processing". AtScale Adaptive Analytics (A3) is most compared with Denodo, Dremio, ThoughtSpot, SAP BusinessObjects Business Intelligence Platform and Kyvos, whereas Spark SQL is most compared with Apache Spark, IBM Db2 Big SQL, SAP HANA, HPE Ezmeral Data Fabric and Netezza Analytics.
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