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 good performance. The nice graphical management console. The long list of ML algorithms."
"Features include machine learning, real time streaming, and data processing."
"With Hadoop-related technologies, we can distribute the workload with multiple commodity hardware."
"The scalability has been the most valuable aspect of the solution."
"The tool's most valuable feature is its speed and efficiency. It's much faster than other tools and excels in parallel data processing. Unlike tools like Python or JavaScript, which may struggle with parallel processing, it allows us to handle large volumes of data with more power easily."
"Spark helps us reduce startup time for our customers and gives a very high ROI in the medium term."
"The main feature that we find valuable is that it is very fast."
"I found the solution stable. We haven't had any problems with it."
"AWS Batch manages the execution of computing workload, including job scheduling, provisioning, and scaling."
"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."
"I know there is always discussion about which language to write applications in and some people do love Scala. However, I don't like it."
"It needs a new interface and a better way to get some data. In terms of writing our scripts, some processes could be faster."
"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."
"Stream processing needs to be developed more in Spark. I have used Flink previously. Flink is better than Spark at stream processing."
"The logging for the observability platform could be better."
"Stability in terms of API (things were difficult, when transitioning from RDD to DataFrames, then to DataSet)."
"At the initial stage, the product provides no container logs to check the activity."
"If you have a Spark session in the background, sometimes it's very hard to kill these sessions because of D allocation."
"When we run a lot of batch jobs, the UI must show the history."
"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."
"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 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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