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."The deployment of the product is easy."
"One of Apache Spark's most valuable features is that it supports in-memory processing, the execution of jobs compared to traditional tools is very fast."
"The most valuable feature of Apache Spark is its flexibility."
"There's a lot of functionality."
"The scalability has been the most valuable aspect of the solution."
"I feel the streaming is its best feature."
"The solution is scalable."
"With Hadoop-related technologies, we can distribute the workload with multiple commodity hardware."
"AWS Batch's deployment was easy."
"We can easily integrate AWS container images into the product."
"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."
"AWS Batch manages the execution of computing workload, including job scheduling, provisioning, and scaling."
"Apache Spark should add some resource management improvements to the algorithms."
"If you have a Spark session in the background, sometimes it's very hard to kill these sessions because of D allocation."
"It's not easy to install."
"When you are working with large, complex tasks, the garbage collection process is slow and affects performance."
"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 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."
"It requires overcoming a significant learning curve due to its robust and feature-rich nature."
"The solution needs to optimize shuffling between workers."
"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."
"AWS Batch needs to improve its documentation."
"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 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.
See our list of best Compute Service vendors.
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.