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 found the solution stable. We haven't had any problems with it."
"The most valuable feature is the Fault Tolerance and easy binding with other processes like Machine Learning, graph analytics."
"Apache Spark provides a very high-quality implementation of distributed data processing."
"With Hadoop-related technologies, we can distribute the workload with multiple commodity hardware."
"The solution has been very stable."
"The data processing framework is good."
"The product's deployment phase is easy."
"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."
"We can easily integrate AWS container images into the product."
"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."
"The product could improve the user interface and make it easier for new users."
"If you have a Spark session in the background, sometimes it's very hard to kill these sessions because of D allocation."
"We've had problems using a Python process to try to access something in a large volume of data. It crashes if somebody gives me the wrong code because it cannot handle a large volume of data."
"Dynamic DataFrame options are not yet available."
"The solution needs to optimize shuffling between workers."
"When you are working with large, complex tasks, the garbage collection process is slow and affects performance."
"The setup I worked on was really complex."
"The solution must improve its performance."
"When we run a lot of batch jobs, the UI must show the history."
"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."
"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 3rd 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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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.
