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."EC2 is flexible when we need to increase its resources (memory, boosting, and storage) based on our usage. That is the power of EC2."
"The most valuable features are the scalability options, low maintenance, and options to upgrade. AWS support is also pretty good. The generation upgrade is pretty simple and standardized."
"Amazon EC2 is really reliable and provides great flexibility."
"We don't have to worry about scalability issues or maintenance or security. It's all taken care of."
"The ability to bring up servers and then do the computation and deposit means we don't have to maintain a data center. Everything is virtual and the security is also taken care of. It helps us to achieve compliance. Being a small startup with the security features that AWS provides helps us with compliance."
"The amount of bandwidth has been most valuable."
"EC2 has the typical advantages of using the cloud. It's easy to provision and set up."
"The product is very mature and organized."
"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 feel the streaming is its best feature."
"This solution provides a clear and convenient syntax for our analytical tasks."
"Apache Spark provides a very high-quality implementation of distributed data processing."
"I appreciate everything about the solution, not just one or two specific features. The solution is highly stable. I rate it a perfect ten. The solution is highly scalable. I rate it a perfect ten. The initial setup was straightforward. I recommend using the solution. Overall, I rate the solution a perfect ten."
"With Spark, we parallelize our operations, efficiently accessing both historical and real-time data."
"It is highly scalable, allowing you to efficiently work with extensive datasets that might be problematic to handle using traditional tools that are memory-constrained."
"Features include machine learning, real time streaming, and data processing."
"One of the challenges is the AMI upgrades."
"The scalability could improve."
"They can build automatic features for ENSS or network drive. They have the Control-M feature. Similarly, they should have a feature for the network drive that can be mapped. I have not seen such a feature. They have a lot of products but those are quite costly. There is no cheaper option available for the EC2 instance for syncing two drives. If these features are available, it would be good."
"In terms of improvement, they could build some client-side desktop tools that provide easier connectivity to Amazon."
"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."
"Amazon EC2 could improve the stability."
"The product needs to improve its cost management."
"Amazon EC2 could improve the console view. The ability to see the console view directly would be helpful, similar to what VMware has. Additionally, when the system is rebooting we are able to see a screenshot of the UI, but it would be a lot better if we could interact directly with the console level."
"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 first start using this solution, it is common to run into memory errors when you are dealing with large amounts of data."
"I would like to see integration with data science platforms to optimize the processing capability for these tasks."
"There were some problems related to the product's compatibility with a few Python libraries."
"The solution must improve its performance."
"We use big data manager but we cannot use it as conditional data so whenever we're trying to fetch the data, it takes a bit of time."
"Apache Spark is very difficult to use. It would require a data engineer. It is not available for every engineer today because they need to understand the different concepts of Spark, which is very, very difficult and it is not easy to learn."
"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."
Amazon EC2 is ranked 4th in Compute Service with 56 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, Apache NiFi, AWS Batch and Google App Engine, 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.
