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."
"I feel the streaming is its best feature."
"It provides a scalable machine learning library."
"There's a lot of functionality."
"The most valuable feature of this solution is its capacity for processing large amounts of data."
"Spark helps us reduce startup time for our customers and gives a very high ROI in the medium term."
"The product’s most valuable features are lazy evaluation and workload distribution."
"It is highly scalable, allowing you to efficiently work with extensive datasets that might be problematic to handle using traditional tools that are memory-constrained."
"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's deployment was easy."
"AWS Batch manages the execution of computing workload, including job scheduling, provisioning, and scaling."
"In data analysis, you need to take real-time data from different data sources. You need to process this in a subsecond, do the transformation in a subsecond, and all that."
"Needs to provide an internal schedule to schedule spark jobs with monitoring capability."
"The setup I worked on was really complex."
"The solution needs to optimize shuffling between workers."
"Apache Spark's GUI and scalability could be improved."
"More ML based algorithms should be added to it, to make it algorithmic-rich for developers."
"It would be beneficial to enhance Spark's capabilities by incorporating models that utilize features not traditionally present in its framework."
"The solution’s integration with other platforms should be improved."
"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."
"When we run a lot of batch jobs, the UI must show the history."
"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."
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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