We performed a comparison between Apache Spark and IBM InfoSphere BigInsights [EOL] based on real PeerSpot user reviews.
Find out what your peers are saying about Apache, Cloudera, Amazon Web Services (AWS) and others in Hadoop."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."
"The solution has been very stable."
"The product’s most valuable feature is the SQL tool. It enables us to create a database and publish it."
"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."
"One of the key features is that Apache Spark is a distributed computing framework. You can help multiple slaves and distribute the workload between them."
"I feel the streaming is its best feature."
"Its scalability and speed are very valuable. You can scale it a lot. It is a great technology for big data. It is definitely better than a lot of earlier warehouse or pipeline solutions, such as Informatica. Spark SQL is very compliant with normal SQL that we have been using over the years. This makes it easy to code in Spark. It is just like using normal SQL. You can use the APIs of Spark or you can directly write SQL code and run it. This is something that I feel is useful in Spark."
"The data processing framework is good."
"InfoSphere Streams was the one core product from the platform in which we were using. We were building a real-time response system and we built it on InfoSphere Streams."
"The solution must improve its performance."
"Its UI can be better. Maintaining the history server is a little cumbersome, and it should be improved. I had issues while looking at the historical tags, which sometimes created problems. You have to separately create a history server and run it. Such things can be made easier. Instead of separately installing the history server, it can be made a part of the whole setup so that whenever you set it up, it becomes available."
"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."
"Stream processing needs to be developed more in Spark. I have used Flink previously. Flink is better than Spark at stream processing."
"At times during the deployment process, the tool goes down, making it look less robust. To take care of the issues in the deployment process, users need to do manual interventions occasionally."
"The logging for the observability platform could be better."
"It should support more programming languages."
"It needs a new interface and a better way to get some data. In terms of writing our scripts, some processes could be faster."
"The UI was not interactive: Responses used to be very slow and hang up at times."
Earn 20 points
Apache Spark is ranked 1st in Hadoop with 60 reviews while IBM InfoSphere BigInsights [EOL] doesn't meet the minimum requirements to be ranked in Hadoop. Apache Spark is rated 8.4, while IBM InfoSphere BigInsights [EOL] is rated 7.6. 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 IBM InfoSphere BigInsights [EOL] writes "The BIQSQL implementation is fully SQL ANSI compliant, but I have found a lot of issues in Fluid Query". Apache Spark is most compared with Spring Boot, AWS Batch, Spark SQL, SAP HANA and Cloudera Distribution for Hadoop, whereas IBM InfoSphere BigInsights [EOL] is most compared with .
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