We performed a comparison between Apache Spark and QueryIO based on real PeerSpot user reviews.
Find out what your peers are saying about Apache, Cloudera, Amazon Web Services (AWS) and others in Hadoop."With Hadoop-related technologies, we can distribute the workload with multiple commodity hardware."
"The fault tolerant feature is provided."
"Features include machine learning, real time streaming, and data processing."
"It is useful for handling large amounts of data. It is very useful for scientific purposes."
"The solution has been very stable."
"The good performance. The nice graphical management console. The long list of ML algorithms."
"It's easy to prepare parallelism in Spark, run the solution with specific parameters, and get good performance."
"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."
"Anyone who has even a little bit of knowledge of the solution can begin to create things. You don't have to be technical to use the solution."
"Apart from the restrictions that come with its in-memory implementation. It has been improved significantly up to version 3.0, which is currently in use."
"At the initial stage, the product provides no container logs to check the activity."
"It's not easy to install."
"It requires overcoming a significant learning curve due to its robust and feature-rich nature."
"Needs to provide an internal schedule to schedule spark jobs with monitoring capability."
"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."
"Apache Spark's GUI and scalability could be improved."
"One limitation is that not all machine learning libraries and models support it."
"There needs to be some simplification of the user interface."
Earn 20 points
Apache Spark is ranked 1st in Hadoop with 60 reviews while QueryIO is ranked 16th in Hadoop. Apache Spark is rated 8.4, while QueryIO is rated 8.0. 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 QueryIO writes "Stable with good connectivity and good integration capabilities". Apache Spark is most compared with Spring Boot, AWS Batch, Spark SQL, SAP HANA and Cloudera Distribution for Hadoop, whereas QueryIO is most compared with Splice Machine.
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