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."It provides a scalable machine learning library."
"It's easy to prepare parallelism in Spark, run the solution with specific parameters, and get good performance."
"Provides a lot of good documentation compared to other solutions."
"The good performance. The nice graphical management console. The long list of ML algorithms."
"DataFrame: Spark SQL gives the leverage to create applications more easily and with less coding effort."
"The main feature that we find valuable is that it is very fast."
"The most valuable feature of this solution is its capacity for processing large amounts of data."
"The product’s most valuable feature is the SQL tool. It enables us to create a database and publish it."
"We appreciate the simple use of containers within this solution, it makes managing the containers quick and easy."
"Fargate itself is a stable product. We are quite satisfied with its performance."
"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."
"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."
"The most valuable feature of AWS Fargate is its ease of use."
"If you create your deployment with a good set of rules for how to scale in, you can just set it and forget it."
"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."
"AWS Fargate is an easy-to-use tool to simplify setup. You only pay for the resources you use. If you need to quickly create, delete, or scale applications without managing resources like EC2 instances, Fargate is the best service to use."
"If you have a Spark session in the background, sometimes it's very hard to kill these sessions because of D allocation."
"The solution must improve its performance."
"Needs to provide an internal schedule to schedule spark jobs with monitoring capability."
"The migration of data between different versions could be improved."
"Apache Spark could improve the connectors that it supports. There are a lot of open-source databases in the market. For example, cloud databases, such as Redshift, Snowflake, and Synapse. Apache Spark should have connectors present to connect to these databases. There are a lot of workarounds required to connect to those databases, but it should have inbuilt connectors."
"When you are working with large, complex tasks, the garbage collection process is slow and affects performance."
"They could improve the issues related to programming language for the platform."
"The solution needs to optimize shuffling between workers."
"We faced challenges in vertically scaling our workload."
"AWS Fargate could improve the privileged mode containers. We had some problems and they were not able to run."
"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."
"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."
"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 8 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 "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 Amazon Corretto, whereas AWS Fargate is most compared with Amazon EC2 Auto Scaling, Amazon EC2, AWS Batch, AWS Lambda and Apache NiFi. See our AWS Fargate vs. Apache Spark report.
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