We performed a comparison between Apache Spark and AWS Fargate 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."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 product is useful for analytics."
"ETL and streaming capabilities."
"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."
"We use it for ETL purposes as well as for implementing the full transformation pipelines."
"With Spark, we parallelize our operations, efficiently accessing both historical and real-time data."
"With Hadoop-related technologies, we can distribute the workload with multiple commodity hardware."
"The solution is scalable."
"The most valuable feature of AWS Fargate is its ease of use."
"AWS Fargate has many valuable services. It does the job with minimal trouble. It's very observable. You can see what's going on and you have logs. You have everything. You can troubleshoot it. It's affordable and it's flexible."
"If you create your deployment with a good set of rules for how to scale in, you can just set it and forget it."
"Fargate itself is a stable product. We are quite satisfied with its performance."
"I like their containerization service. You can use Docker or something similar and deploy quickly without the know-how related to, for example, Kubernetes. If you use AKS or Kubernetes, then you have to have the know-how. But for Fargate, you don't need to have the know-how there. You just deploy the container or the image, and then you have the container, and you can use it as AWS takes care of the rest. This makes it easier for those getting started or if you don't have a strong DevOps team inside your organization."
"We appreciate the simple use of containers within this solution, it makes managing the containers quick and easy."
"The initial setup was not easy."
"It requires overcoming a significant learning curve due to its robust and feature-rich nature."
"The logging for the observability platform could be better."
"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."
"Spark could be improved by adding support for other open-source storage layers than Delta Lake."
"At the initial stage, the product provides no container logs to check the activity."
"Apache Spark's GUI and scalability could be improved."
"When you are working with large, complex tasks, the garbage collection process is slow and affects performance."
"We faced challenges in vertically scaling our workload."
"The main area for improvement is the cost, which could be lowered to be more competitive with other major cloud providers."
"We would like to see some improvement in the process documents that are provided with this product, particularly for auto-scaling and other configuration tools that are a bit complicated."
"I would like to see the older dashboard instead of the newer version. I don't like the new dashboard."
"I heard from my team that it's not easy to predict the cost. That is the only issue we have with AWS Fargate, but I think that's acceptable. AWS Fargate isn't user-friendly. Anything related to Software as a Service or microservice architecture is not easy to implement. You're required to have DevOps from your side to implement the solution. AWS Fargate is just a temporary solution for us. When we grow to a certain level, we may use AKS for better control."
"AWS Fargate could improve the privileged mode containers. We had some problems and they were not able to run."
Apache Spark is ranked 5th in Compute Service with 60 reviews while AWS Fargate is ranked 6th in Compute Service with 6 reviews. Apache Spark is rated 8.4, while AWS Fargate is rated 8.6. 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 Fargate writes "Efficiently auto-scales and good performance". Apache Spark is most compared with Spring Boot, AWS Batch, Spark SQL, SAP HANA and Amazon EC2, whereas AWS Fargate is most compared with Amazon EC2 Auto Scaling, Amazon EC2, AWS Lambda, AWS Batch and Apache NiFi. See our AWS Fargate vs. Apache Spark report.
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