We performed a comparison between Amazon EC2 Auto Scaling 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."It has the best auto-scaling features."
"We appreciate that this solution allows us to run all of our severs through it, meaning that our workloads are mainly on the EC2 instance only."
"We use the solution to increase CPU and memory size."
"What we have found most valuable are the purchasing of usage at the time and small storage."
"Auto-scaling is a good feature."
"Amazon EC2 Auto Scaling has good integration."
"The solution removes the need for hardware. We can easily create servers or machines. Just by clicking or specifying our requirements, like memory size or disk space, it's set up for us. The tool eliminates the need for hardware. We can choose what we need and pay as we use it. It is flexible and can integrate with any product."
"The initial setup is straightforward."
"The solution is scalable."
"I found the solution stable. We haven't had any problems with it."
"The processing time is very much improved over the data warehouse solution that we were using."
"The tool's most valuable feature is its speed and efficiency. It's much faster than other tools and excels in parallel data processing. Unlike tools like Python or JavaScript, which may struggle with parallel processing, it allows us to handle large volumes of data with more power easily."
"The product's deployment phase is easy."
"The main feature that we find valuable is that it is very fast."
"There's a lot of functionality."
"Spark can handle small to huge data and is suitable for any size of company."
"The product's setup is complex for an intermediate user."
"There should be an AWS instance in South Africa, where the latency would be even lower. It might happen soon since AWS has recently opened more data centres in Nigeria. AWS may extend its reach to South Africa, and offer hosted CLI servers there. Most of the problems with AWS are not to do with the solution itself but with configuration. It is something on design, more or less."
"The licensing cost is expensive."
"We have found that the sizing in Amazon EC2 Auto Scaling is far off. For example, we will see some at one terabyte and the other one is two terabytes. There is nothing between one and two terabytes. Sometimes it's a struggle if I need one and a half, I still am supposed to pay for two. There are five terabytes, six terabytes, and 12 terabytes, and if I need something at eight or nine, I'm still paying 30 to 40 percent more by taking the one which is 12 terabytes. Microsoft Azure does similar sizes but the gap can be more, such as six terabytes, and the next one is 12 terabytes."
"The tool must provide proper guidelines to troubleshoot connectivity issues."
"The product's technical support needs to be better."
"The technical support needs to be improved."
"The documentation for this solution could be improved. For example, it is difficult to find documentation for integration with applications."
"It's not easy to install."
"I would like to see integration with data science platforms to optimize the processing capability for these tasks."
"When you are working with large, complex tasks, the garbage collection process is slow and affects performance."
"This solution currently cannot support or distribute neural network related models, or deep learning related algorithms. We would like this functionality to be developed."
"The setup I worked on was really complex."
"If you have a Spark session in the background, sometimes it's very hard to kill these sessions because of D allocation."
"Technical expertise from an engineer is required to deploy and run high-tech tools, like Informatica, on Apache Spark, making it an area where improvements are required to make the process easier for users."
"The solution needs to optimize shuffling between workers."
Amazon EC2 Auto Scaling is ranked 2nd in Compute Service with 37 reviews while Apache Spark is ranked 5th in Compute Service with 60 reviews. Amazon EC2 Auto Scaling is rated 8.8, while Apache Spark is rated 8.4. The top reviewer of Amazon EC2 Auto Scaling writes "Well-documented setup process and highly stable solution". On the other hand, the top reviewer of Apache Spark writes "Reliable, able to expand, and handle large amounts of data well". Amazon EC2 Auto Scaling is most compared with AWS Fargate, AWS Lambda, AWS Batch, Amazon Elastic Inference and Oracle Compute Cloud Service, whereas Apache Spark is most compared with Spring Boot, AWS Batch, Spark SQL, SAP HANA and Cloudera Distribution for Hadoop. See our Amazon EC2 Auto Scaling 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.
