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."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."
"The scalability has been the most valuable aspect of the solution."
"I like that it can handle multiple tasks parallelly. I also like the automation feature. JavaScript also helps with the parallel streaming of the library."
"We use Spark to process data from different data sources."
"The features we find most valuable are the machine learning, data learning, and Spark Analytics."
"Apache Spark can do large volume interactive data analysis."
"ETL and streaming capabilities."
"The most valuable feature of Apache Spark is its ease of use."
"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."
"If you create your deployment with a good set of rules for how to scale in, you can just set it and forget it."
"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 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 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."
"The most valuable feature of AWS Fargate is its ease of use."
"We appreciate the simple use of containers within this solution, it makes managing the containers quick and easy."
"It should support more programming languages."
"It would be beneficial to enhance Spark's capabilities by incorporating models that utilize features not traditionally present in its framework."
"The setup I worked on was really complex."
"Apache Spark's GUI and scalability could be improved."
"When you first start using this solution, it is common to run into memory errors when you are dealing with large amounts of data."
"We've had problems using a Python process to try to access something in a large volume of data. It crashes if somebody gives me the wrong code because it cannot handle a large volume of data."
"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."
"It's not easy to install."
"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."
"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."
"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."
"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 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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