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 Cloudera, Apache, Amazon and others in Hadoop."I feel the streaming is its best feature."
"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 most valuable feature of this solution is its capacity for processing large amounts of data."
"The most valuable feature of Apache Spark is its flexibility."
"The most valuable feature is the Fault Tolerance and easy binding with other processes like Machine Learning, graph analytics."
"We use Spark to process data from different data sources."
"AI libraries are the most valuable. They provide extensibility and usability. Spark has a lot of connectors, which is a very important and useful feature for AI. You need to connect a lot of points for AI, and you have to get data from those systems. Connectors are very wide in Spark. With a Spark cluster, you can get fast results, especially for AI."
"It is useful for handling large amounts of data. It is very useful for scientific purposes."
"The GUI interface is nice and easy to use."
"One limitation is that not all machine learning libraries and models support it."
"It needs a new interface and a better way to get some data. In terms of writing our scripts, some processes could be faster."
"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."
"In data analysis, you need to take real-time data from different data sources. You need to process this in a subsecond, do the transformation in a subsecond, and all that."
"Apache Spark is very difficult to use. It would require a data engineer. It is not available for every engineer today because they need to understand the different concepts of Spark, which is very, very difficult and it is not easy to learn."
"The setup I worked on was really complex."
"The solution needs to optimize shuffling between workers."
"The solution must improve its performance."
"The product was not able to meet our 10 second refresh requirements."
"There was an issue with the incremental aggregation not working as indicated."
"The organization of the icons is not saved across users."
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Apache Spark is ranked 2nd in Hadoop with 58 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, SAP BusinessObjects Business Intelligence Platform, ThoughtSpot and Alation Data Catalog.
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