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."The deployment of the product is easy."
"Apache Spark provides a very high-quality implementation of distributed data processing."
"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 can handle small to huge data and is suitable for any size of company."
"It provides a scalable machine learning library."
"The features we find most valuable are the machine learning, data learning, and Spark Analytics."
"With Hadoop-related technologies, we can distribute the workload with multiple commodity hardware."
"I appreciate everything about the solution, not just one or two specific features. The solution is highly stable. I rate it a perfect ten. The solution is highly scalable. I rate it a perfect ten. The initial setup was straightforward. I recommend using the solution. Overall, I rate the solution a perfect ten."
"The solution helps us to manage and scale automatically whenever there is a limit to the increase in the application workflow."
"Stream processing needs to be developed more in Spark. I have used Flink previously. Flink is better than Spark at stream processing."
"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."
"We are building our own queries on Spark, and it can be improved in terms of query handling."
"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."
"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."
"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."
"The logging for the observability platform could be better."
"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 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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We monitor all Compute Service 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.
