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."This solution provides a clear and convenient syntax for our analytical tasks."
"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 solution has been very stable."
"Its scalability and speed are very valuable. You can scale it a lot. It is a great technology for big data. It is definitely better than a lot of earlier warehouse or pipeline solutions, such as Informatica. Spark SQL is very compliant with normal SQL that we have been using over the years. This makes it easy to code in Spark. It is just like using normal SQL. You can use the APIs of Spark or you can directly write SQL code and run it. This is something that I feel is useful in Spark."
"The data processing framework is good."
"Provides a lot of good documentation compared to other solutions."
"The most valuable feature of Apache Spark is its flexibility."
"Apache Spark can do large volume interactive data analysis."
"AWS Batch's deployment was easy."
"AWS Batch manages the execution of computing workload, including job scheduling, provisioning, and scaling."
"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."
"The solution’s integration with other platforms should be improved."
"Needs to provide an internal schedule to schedule spark jobs with monitoring capability."
"When using Spark, users may need to write their own parallelization logic, which requires additional effort and expertise."
"Apart from the restrictions that come with its in-memory implementation. It has been improved significantly up to version 3.0, which is currently in use."
"Stability in terms of API (things were difficult, when transitioning from RDD to DataFrames, then to DataSet)."
"There were some problems related to the product's compatibility with a few Python libraries."
"They could improve the issues related to programming language for the platform."
"The graphical user interface (UI) could be a bit more clear. It's very hard to figure out the execution logs and understand how long it takes to send everything. If an execution is lost, it's not so easy to understand why or where it went. I have to manually drill down on the data processes which takes a lot of time. Maybe there could be like a metrics monitor, or maybe the whole log analysis could be improved to make it easier to understand and navigate."
"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."
"AWS Batch needs to improve its documentation."
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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