We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
"The processing time is very much improved over the data warehouse solution that we were using."
"I feel the streaming is its best feature."
"The solution is very stable."
"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 features we find most valuable are the machine learning, data learning, and Spark Analytics."
"The main feature that we find valuable is that it is very fast."
"It is a stable solution."
"The performance is one of the most important features. It has an API to process the data in a functional manner."
"The speed of getting data."
"Data validation and ease of use are the most valuable features."
"Overall the solution is excellent."
"The graphical user interface (UI) could be a bit more clear. It's very hard to figure out the execution logs and understand how long it takes to send everything. If an execution is lost, it's not so easy to understand why or where it went. I have to manually drill down on the data processes which takes a lot of time. Maybe there could be like a metrics monitor, or maybe the whole log analysis could be improved to make it easier to understand and navigate."
"The logging for the observability platform could be better."
"I would like to see integration with data science platforms to optimize the processing capability for these tasks."
"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."
"The solution needs to optimize shuffling between workers."
"When you want to extract data from your HDFS and other sources then it is kind of tricky because you have to connect with those sources."
"It's not easy to install."
"We use big data manager but we cannot use it as conditional data so whenever we're trying to fetch the data, it takes a bit of time."
"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."
"There should be better integration with other solutions."
"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."
"Anything to improve the GUI would be helpful."
"The solution needs to include graphing capabilities. Including financial charts would help improve everything overall."
Spark provides programmers with an application programming interface centered on a data structure called the resilient distributed dataset (RDD), a read-only multiset of data items distributed over a cluster of machines, that is maintained in a fault-tolerant way. It was developed in response to limitations in the MapReduce cluster computing paradigm, which forces a particular linear dataflowstructure on distributed programs: MapReduce programs read input data from disk, map a function across the data, reduce the results of the map, and store reduction results on disk. Spark's RDDs function as a working set for distributed programs that offers a (deliberately) restricted form of distributed shared memory
Apache Spark is ranked 1st in Hadoop with 10 reviews while Spark SQL is ranked 3rd in Hadoop with 5 reviews. Apache Spark is rated 8.6, while Spark SQL is rated 7.0. The top reviewer of Apache Spark writes "Good Streaming features enable to enter data and analysis within Spark Stream". On the other hand, the top reviewer of Spark SQL writes "GUI could be improved. Useful for speedily processing big data". Apache Spark is most compared with Spring Boot, Azure Stream Analytics, AWS Batch, AWS Lambda and SAP HANA, whereas Spark SQL is most compared with IBM Db2 Big SQL, Amazon EMR, Informatica Big Data Parser, Netezza Analytics and AtScale Adaptive Analytics (A3). See our Apache Spark vs. Spark SQL report.
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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.