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."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."
"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."
"Spark can handle small to huge data and is suitable for any size of company."
"The scalability has been the most valuable aspect of the solution."
"This solution provides a clear and convenient syntax for our analytical tasks."
"The most valuable feature of Apache Spark is its memory processing because it processes data over RAM rather than disk, which is much more efficient and fast."
"The product’s most valuable features are lazy evaluation and workload distribution."
"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."
"AWS Batch manages the execution of computing workload, including job scheduling, provisioning, and scaling."
"I know there is always discussion about which language to write applications in and some people do love Scala. However, I don't like it."
"The product could improve the user interface and make it easier for new users."
"There were some problems related to the product's compatibility with a few Python libraries."
"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."
"Spark could be improved by adding support for other open-source storage layers than Delta Lake."
"Stream processing needs to be developed more in Spark. I have used Flink previously. Flink is better than Spark at stream processing."
"The initial setup was not easy."
"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."
"AWS Batch needs to improve its documentation."
"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."
"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 Azure Stream Analytics, 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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