We performed a comparison between Apache Spark and AtScale Adaptive Analytics (A3) based on real PeerSpot user reviews.
Find out what your peers are saying about Apache, Cloudera, Amazon Web Services (AWS) and others in Hadoop."Apache Spark provides a very high-quality implementation of distributed data processing."
"Features include machine learning, real time streaming, and data processing."
"The product's deployment phase is easy."
"The distribution of tasks, like the seamless map-reduce functionality, is quite impressive."
"With Hadoop-related technologies, we can distribute the workload with multiple commodity hardware."
"Apache Spark can do large volume interactive data analysis."
"One of Apache Spark's most valuable features is that it supports in-memory processing, the execution of jobs compared to traditional tools is very fast."
"The most valuable feature of Apache Spark is its memory processing because it processes data over RAM rather than disk, which is much more efficient and fast."
"The GUI interface is nice and easy to use."
"If you have a Spark session in the background, sometimes it's very hard to kill these sessions because of D allocation."
"The setup I worked on was really complex."
"There could be enhancements in optimization techniques, as there are some limitations in this area that could be addressed to further refine Spark's performance."
"The solution’s integration with other platforms should be improved."
"Apache Spark can improve the use case scenarios from the website. There is not any information on how you can use the solution across the relational databases toward multiple databases."
"Apache Spark could potentially improve in terms of user-friendliness, particularly for individuals with a SQL background. While it's suitable for those with programming knowledge, making it more accessible to those without extensive programming skills could be beneficial."
"I know there is always discussion about which language to write applications in and some people do love Scala. However, I don't like it."
"Needs to provide an internal schedule to schedule spark jobs with monitoring capability."
"The organization of the icons is not saved across users."
"There was an issue with the incremental aggregation not working as indicated."
"The product was not able to meet our 10 second refresh requirements."
Earn 20 points
Apache Spark is ranked 1st in Hadoop with 60 reviews while AtScale Adaptive Analytics (A3) is ranked 5th in Data Virtualization. Apache Spark is rated 8.4, while AtScale Adaptive Analytics (A3) is rated 5.0. The top reviewer of Apache Spark writes "Reliable, able to expand, and handle large amounts of data well". On the other hand, 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". Apache Spark is most compared with Spring Boot, AWS Batch, Spark SQL, SAP HANA and Cloudera Distribution for Hadoop, whereas AtScale Adaptive Analytics (A3) is most compared with Denodo, Dremio, ThoughtSpot, SAP BusinessObjects Business Intelligence Platform and Alation Data Catalog.
We monitor all Hadoop reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.