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."I found the solution stable. We haven't had any problems with it."
"Apache Spark can do large volume interactive data analysis."
"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."
"With Spark, we parallelize our operations, efficiently accessing both historical and real-time data."
"The memory processing engine is the solution's most valuable aspect. It processes everything extremely fast, and it's in the cluster itself. It acts as a memory engine and is very effective in processing data correctly."
"The distribution of tasks, like the seamless map-reduce functionality, is quite impressive."
"Spark helps us reduce startup time for our customers and gives a very high ROI in the medium term."
"The product's deployment phase is easy."
"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."
"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."
"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 most valuable feature of Fargate is that it's self-managed. You don't have to configure your own clusters or deploy any Kubernetes clusters. This simplifies the initial deployment and scaling process."
"The most valuable feature of AWS Fargate is its ease of use."
"This solution currently cannot support or distribute neural network related models, or deep learning related algorithms. We would like this functionality to be developed."
"When you want to extract data from your HDFS and other sources then it is kind of tricky because you have to connect with those sources."
"The solution’s integration with other platforms should be improved."
"At times during the deployment process, the tool goes down, making it look less robust. To take care of the issues in the deployment process, users need to do manual interventions occasionally."
"At the initial stage, the product provides no container logs to check the activity."
"The logging for the observability platform could be better."
"We are building our own queries on Spark, and it can be improved in terms of query handling."
"Apache Spark's GUI and scalability could be improved."
"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."
"If there are any options to manage containers, that would be good. That relates more to the cost point. For example, over the next three months, I'll be making a comparison between solutions like CAST AI and other software-as-a-service platforms that offer Kubernetes management with an emphasis on cost reduction."
"AWS Fargate could improve the privileged mode containers. We had some problems and they were not able to run."
"We faced challenges in vertically scaling our workload."
"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."
"The main area for improvement is the cost, which could be lowered to be more competitive with other major cloud providers."
Apache Spark is ranked 5th in Compute Service with 60 reviews while AWS Fargate is ranked 6th in Compute Service with 7 reviews. Apache Spark is rated 8.4, while AWS Fargate is rated 8.8. 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 "Offers serverless capabilities, self-managed, simplifies ease of use and integrates with other AWS services". Apache Spark is most compared with Spring Boot, AWS Batch, Spark SQL, SAP HANA and Vert.x, 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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