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."
"There's a lot of functionality."
"The solution is scalable."
"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."
"Now, when we're tackling sentiment analysis using NLP technologies, we deal with unstructured data—customer chats, feedback on promotions or demos, and even media like images, audio, and video files. For processing such data, we rely on PySpark. Beneath the surface, Spark functions as a compute engine with in-memory processing capabilities, enhancing performance through features like broadcasting and caching. It's become a crucial tool, widely adopted by 90% of companies for a decade or more."
"Apache Spark can do large volume interactive data analysis."
"The features we find most valuable are the machine learning, data learning, and Spark Analytics."
"Features include machine learning, real time streaming, and data processing."
"We can easily integrate AWS container images into the product."
"AWS Batch manages the execution of computing workload, including job scheduling, provisioning, and scaling."
"AWS Batch's deployment was easy."
"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."
"When you are working with large, complex tasks, the garbage collection process is slow and affects performance."
"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."
"More ML based algorithms should be added to it, to make it algorithmic-rich for developers."
"One limitation is that not all machine learning libraries and models support it."
"They could improve the issues related to programming language for the platform."
"The solution’s integration with other platforms should be improved."
"Apache Spark could potentially improve in terms of user-friendliness, particularly for individuals with a SQL background. While it's suitable for those with programming knowledge, making it more accessible to those without extensive programming skills could be beneficial."
"It should support more programming languages."
"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."
"AWS Batch needs to improve its documentation."
"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 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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