We performed a comparison between Amazon EC2 and Apache Spark 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."The serverless architecture solutions are most valuable, and the ability to start with little cost, and then expand as needed."
"The most important aspects are that the solution is scalable and easy to manage."
"My company uses the tool for cloud monitoring. We have a production, staging, deployment, and testing environment in AWS. However, we do not use the managed service of AWS yet. My team uses the required parameters for security like VPC, firewall, gates of security as well as the external layer of the app."
"The product is very mature and organized."
"What I found most valuable in Amazon EC2 is that you only pay for what you use, versus an on-premise deployment that requires you to pay for the cost of the server. When it's on-premise, you'll need to meet more specifications and requirements, and the purchasing process even takes time. As Amazon EC2 is cloud-based, you'll only pay when you use the service."
"I believe that cloud solutions are better than physical servers."
"The tool's performance, reliability, security and flexibility are good. We can use it remotely. The autoscaling functionality of EC2 is quite good. I appreciate the DevOps suite for tracking development tasks. This functionality is important for pure software development."
"Stable, scalable, and simple to implement."
"The main feature that we find valuable is that it is very fast."
"Its scalability and speed are very valuable. You can scale it a lot. It is a great technology for big data. It is definitely better than a lot of earlier warehouse or pipeline solutions, such as Informatica. Spark SQL is very compliant with normal SQL that we have been using over the years. This makes it easy to code in Spark. It is just like using normal SQL. You can use the APIs of Spark or you can directly write SQL code and run it. This is something that I feel is useful in Spark."
"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."
"The solution is scalable."
"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 good performance. The nice graphical management console. The long list of ML algorithms."
"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."
"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."
"One of the challenges is the AMI upgrades."
"Amazon EC2's pricing could be better."
"Regional acceleration could improve. If I am hosting a website and I want the experience to be faster they should have this feature to allow for increased speeds."
"The scalability could improve."
"Amazon EC2 is very expensive, and it would be helpful if they decreased the pricing."
"The ease of migrating applications could be improved."
"I think the whole AWS stack is very disconnected from each other. in the .NET space, everything just works nicely together. In the AWS stack, there is a lot of head scratching."
"Technical itself could be a bit more helpful, especially when it comes to integration assistance. When we talk to the technical team, often it's some issue with integration and they'll tell us to talk to the other company. Often, the other company will look at everything and not see an issue from their end and then we are at an impasse."
"The graphical user interface (UI) could be a bit more clear. It's very hard to figure out the execution logs and understand how long it takes to send everything. If an execution is lost, it's not so easy to understand why or where it went. I have to manually drill down on the data processes which takes a lot of time. Maybe there could be like a metrics monitor, or maybe the whole log analysis could be improved to make it easier to understand and navigate."
"Apart from the restrictions that come with its in-memory implementation. It has been improved significantly up to version 3.0, which is currently in use."
"At the initial stage, the product provides no container logs to check the activity."
"The management tools could use improvement. Some of the debugging tools need some work as well. They need to be more descriptive."
"Include more machine learning algorithms and the ability to handle streaming of data versus micro batch processing."
"Its UI can be better. Maintaining the history server is a little cumbersome, and it should be improved. I had issues while looking at the historical tags, which sometimes created problems. You have to separately create a history server and run it. Such things can be made easier. Instead of separately installing the history server, it can be made a part of the whole setup so that whenever you set it up, it becomes available."
"The initial setup was not easy."
"If you have a Spark session in the background, sometimes it's very hard to kill these sessions because of D allocation."
Amazon EC2 is ranked 3rd in Compute Service with 60 reviews while Apache Spark is ranked 5th in Compute Service with 60 reviews. Amazon EC2 is rated 8.6, while Apache Spark is rated 8.4. The top reviewer of Amazon EC2 writes "Easy to scale and valuable features include the security group and key management". On the other hand, the top reviewer of Apache Spark writes "Reliable, able to expand, and handle large amounts of data well". Amazon EC2 is most compared with AWS Fargate, AWS Lambda, AWS Batch and Apache NiFi, whereas Apache Spark is most compared with Spring Boot, AWS Batch, Spark SQL, SAP HANA and Jakarta EE. See our Amazon EC2 vs. Apache Spark report.
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We monitor all Compute Service reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.
