We performed a comparison between Apache Spark and Spot Ocean based on real PeerSpot user reviews.
Find out what your peers are saying about Amazon Web Services (AWS), Apache, Zadara and others in Compute Service."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."
"Spark helps us reduce startup time for our customers and gives a very high ROI in the medium term."
"The solution has been 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 product’s most valuable features are lazy evaluation and workload distribution."
"The deployment of the product is easy."
"I like that it can handle multiple tasks parallelly. I also like the automation feature. JavaScript also helps with the parallel streaming of the library."
"I found the solution stable. We haven't had any problems with it."
"The solution helps us to manage and scale automatically whenever there is a limit to the increase in the application workflow."
"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 would like to see integration with data science platforms to optimize the processing capability for these tasks."
"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."
"Apache Spark could improve the connectors that it supports. There are a lot of open-source databases in the market. For example, cloud databases, such as Redshift, Snowflake, and Synapse. Apache Spark should have connectors present to connect to these databases. There are a lot of workarounds required to connect to those databases, but it should have inbuilt connectors."
"One limitation is that not all machine learning libraries and models support it."
"Technical expertise from an engineer is required to deploy and run high-tech tools, like Informatica, on Apache Spark, making it an area where improvements are required to make the process easier for users."
"Apache Spark should add some resource management improvements to the algorithms."
"Apache Spark's GUI and scalability could be improved."
"The solution doesn't have support from OCI, and it should start working to onboard OCI."
Apache Spark is ranked 5th in Compute Service with 60 reviews while Spot Ocean is ranked 11th in Compute Service with 1 review. Apache Spark is rated 8.4, while Spot Ocean is rated 7.0. 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 Spot Ocean writes "Used to manage Kubernetes infrastructure, but it doesn't have support from OCI". Apache Spark is most compared with Spring Boot, AWS Batch, Spark SQL, SAP HANA and Cloudera Distribution for Hadoop, whereas Spot Ocean is most compared with Spot Elastigroup and Spot Eco.
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