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."Amazon EC2 Auto Scaling operates at a different level, working in parallel to efficiently manage workload distribution. Primarily, it focuses on orchestration rather than directly managing EC2 instances for deployment and configuration. It uses automated processes to deploy and manage ports, leveraging Application Load Balancers to effectively handle data communication and management."
"Can handle traffic spikes so the system doesn't overload."
"The product’s most valuable feature is the seamless resizing of web connection."
"The solution is scalable."
"The product is flexible."
"The solution is highly scalable."
"The most useful feature is elasticity. You can scale up or down based on traffic."
"The integration capabilities are good."
"I feel the streaming is its best feature."
"The most valuable feature of Apache Spark is its flexibility."
"The product is useful for analytics."
"The features we find most valuable are the machine learning, data learning, and Spark Analytics."
"The most valuable feature of this solution is its capacity for processing large amounts of data."
"DataFrame: Spark SQL gives the leverage to create applications more easily and with less coding effort."
"The scalability has been the most valuable aspect of the solution."
"The distribution of tasks, like the seamless map-reduce functionality, is quite impressive."
"It should work for the cloud, cloud monitoring features, and DevOps processes. It should automatically enable features for downscaling and upscaling."
"The product should improve vertical scaling features."
"The solution's configuration process could be better."
"The spinning up in the solution can be much faster...The product should have a faster scalability option."
"The primary area for improvement is the pricing model."
"Scalability can be improved."
"The launch configuration feature doesn't work properly. It needs to improve the load configuration feature along with launch templates. The tool needs to tag feature as well."
"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."
"When you want to extract data from your HDFS and other sources then it is kind of tricky because you have to connect with those sources."
"When using Spark, users may need to write their own parallelization logic, which requires additional effort and expertise."
"When you first start using this solution, it is common to run into memory errors when you are dealing with large amounts of data."
"The product could improve the user interface and make it easier for new users."
"Dynamic DataFrame options are not yet available."
"The solution needs to optimize shuffling between workers."
"The setup I worked on was really complex."
"We are building our own queries on Spark, and it can be improved in terms of query handling."
Amazon EC2 Auto Scaling is ranked 2nd in Compute Service with 39 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, Oracle Compute Cloud Service and Amazon Elastic Inference, 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.
See our list of best Compute Service vendors.
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.
