"The most valuable feature of Apache Spark is its ease of use."
"It is useful for handling large amounts of data. It is very useful for scientific purposes."
"The solution has been very stable."
"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."
"Spark helps us reduce startup time for our customers and gives a very high ROI in the medium term."
"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."
"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."
"Spark can handle small to huge data and is suitable for any size of company."
"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."
"It's not easy to install."
"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."
"The initial setup was not easy."
"Stream processing needs to be developed more in Spark. I have used Flink previously. Flink is better than Spark at stream processing."
"Spark could be improved by adding support for other open-source storage layers than Delta Lake."
"When you are working with large, complex tasks, the garbage collection process is slow and affects performance."
"The logging for the observability platform could be better."
"Apache Spark can improve the use case scenarios from the website. There is not any information on how you can use the solution across the relational databases toward multiple databases."
"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 2nd in Compute Service with 11 reviews while AWS Fargate is ranked 8th in Compute Service with 2 reviews. Apache Spark is rated 8.0, while AWS Fargate is rated 9.0. The top reviewer of Apache Spark writes "Provides fast aggregations, AI libraries, and a lot of connectors". On the other hand, the top reviewer of AWS Fargate writes "A serverless, pay-as-you-go compute engine that you can deploy quickly". Apache Spark is most compared with Spring Boot, Azure Stream Analytics, AWS Lambda, AWS Batch and Spring MVC, whereas AWS Fargate is most compared with Amazon EC2 Auto Scaling, AWS Batch, Amazon EC2, Apache NiFi and Amazon Elastic Inference. See our AWS Fargate vs. Apache Spark report.
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