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."The solution is highly scalable."
"The feature I found most valuable was the vertical and horizontal scaling."
"The monitoring tool is helpful."
"The documentation is good."
"The auto-scaling feature is particularly useful. Additionally, CloudWatch and CloudTrail are also important features for us."
"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."
"The product's most valuable features are high availability and persistence."
"Service for launching or terminating Amazon EC2 instances, with good scalability and stability."
"This solution provides a clear and convenient syntax for our analytical tasks."
"Apache Spark can do large volume interactive data analysis."
"Features include machine learning, real time streaming, and data processing."
"With Hadoop-related technologies, we can distribute the workload with multiple commodity hardware."
"The features we find most valuable are the machine learning, data learning, and Spark Analytics."
"Provides a lot of good documentation compared to other solutions."
"The most valuable feature is the Fault Tolerance and easy binding with other processes like Machine Learning, graph analytics."
"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 solution's pricing is expensive. You pay based on how much you use it, like paying for the time or hours you use the service. There's no need to buy hardware separately."
"Its stability and scalability need improvement."
"The support to manage the processes could be better."
"What could be improved in Amazon EC2 Auto Scaling is its fees."
"Scalability can be improved."
"Could integrate more with other platforms."
"Amazon EC2 Auto Scaling can provide more discounts when using the machines the solution uses."
"The solution's configuration process could be better."
"I know there is always discussion about which language to write applications in and some people do love Scala. However, I don't like it."
"It requires overcoming a significant learning curve due to its robust and feature-rich nature."
"Apache Spark provides very good performance The tuning phase is still tricky."
"Apache Spark could potentially improve in terms of user-friendliness, particularly for individuals with a SQL background. While it's suitable for those with programming knowledge, making it more accessible to those without extensive programming skills could be beneficial."
"We are building our own queries on Spark, and it can be improved in terms of query handling."
"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."
"It should support more programming languages."
"The solution’s integration with other platforms should be improved."
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.
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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.
