We performed a comparison between Apache Spark and AWS Batch based on real PeerSpot user reviews.
Find out in this report how the two Compute Service solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."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."
"The product's deployment phase is easy."
"The product’s most valuable features are lazy evaluation and workload distribution."
"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."
"The solution has been very stable."
"There's a lot of functionality."
"The product is useful for analytics."
"The main feature that we find valuable is that it is very fast."
"AWS Batch manages the execution of computing workload, including job scheduling, provisioning, and scaling."
"AWS Batch's deployment was easy."
"There is one other feature in confirmation or call confirmation where you can have templates of what you want to do and just modify those to customize it to your needs. And these templates basically make it a lot easier for you to get started."
"We can easily integrate AWS container images into the product."
"Dynamic DataFrame options are not yet available."
"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."
"It's not easy to install."
"Apache Spark's GUI and scalability could be improved."
"I would like to see integration with data science platforms to optimize the processing capability for these tasks."
"When you are working with large, complex tasks, the garbage collection process is slow and affects performance."
"When using Spark, users may need to write their own parallelization logic, which requires additional effort and expertise."
"The solution’s integration with other platforms should be improved."
"The main drawback to using AWS Batch would be the cost. It will be more expensive in some cases than using an HPC. It's more amenable to cases where you have spot requirements."
"When we run a lot of batch jobs, the UI must show the history."
"AWS Batch needs to improve its documentation."
"The solution should include better and seamless integration with other AWS services, like Amazon S3 data storage and EC2 compute resources."
Apache Spark is ranked 5th in Compute Service with 60 reviews while AWS Batch is ranked 4th in Compute Service with 4 reviews. Apache Spark is rated 8.4, while AWS Batch is rated 9.0. 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 AWS Batch writes "User-friendly, good customization and offers exceptional scalability, allowing users to run jobs ranging from 32 cores to over 2,000 cores". Apache Spark is most compared with Spring Boot, Spark SQL, SAP HANA, Cloudera Distribution for Hadoop and AWS Lambda, whereas AWS Batch is most compared with AWS Lambda, AWS Fargate, Oracle Compute Cloud Service, Amazon EC2 Auto Scaling and Amazon EC2. See our AWS Batch vs. Apache Spark report.
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