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 most crucial feature for us is the streaming capability. It serves as a fundamental aspect that allows us to exert control over our operations."
"The deployment of the product is easy."
"DataFrame: Spark SQL gives the leverage to create applications more easily and with less coding effort."
"Features include machine learning, real time streaming, and data processing."
"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."
"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."
"The main feature that we find valuable is that it is very fast."
"AWS Batch's deployment was easy."
"We can easily integrate AWS container images into the product."
"AWS Batch manages the execution of computing workload, including job scheduling, provisioning, and scaling."
"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'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."
"When you first start using this solution, it is common to run into memory errors when you are dealing with large amounts of data."
"We are building our own queries on Spark, and it can be improved in terms of query handling."
"Stream processing needs to be developed more in Spark. I have used Flink previously. Flink is better than Spark at stream processing."
"I would like to see integration with data science platforms to optimize the processing capability for these tasks."
"Technical expertise from an engineer is required to deploy and run high-tech tools, like Informatica, on Apache Spark, making it an area where improvements are required to make the process easier for users."
"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."
"When using Spark, users may need to write their own parallelization logic, which requires additional effort and expertise."
"AWS Batch needs to improve its documentation."
"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."
"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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