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."
"What we have found most valuable are the purchasing of usage at the time and small storage."
"The product's most valuable features are high availability and persistence."
"The solution includes many features for configuring networks and VPCs."
"The feature I found most valuable was the vertical and horizontal scaling."
"Most of what I've deployed are CI/CD pipelines. AWS is scalable. You can always increase or adjust the resources to meet the specific requirements. I also like choosing an instance in any location, preferably the closest one. We don't have any AWS locations in South Africa, but the latency is about the same as hosting in Europe."
"It has the best auto-scaling features."
"The initial setup of Amazon EC2 Auto Scaling is easy...Since we are an enterprise-sized company and a client of Amazon, the response from the technical support team was immediate."
"The most valuable feature of Apache Spark is its ease of use."
"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."
"The scalability has been the most valuable aspect of the solution."
"It provides a scalable machine learning library."
"I feel the streaming is its best feature."
"One of the key features is that Apache Spark is a distributed computing framework. You can help multiple slaves and distribute the workload between them."
"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."
"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."
"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."
"The product's setup is complex for an intermediate user."
"The pricing could be reduced."
"There is room for improvement in the scalability."
"We would like to see improvement in the UI for this solution, so that it is more user-friendly."
"When creating a new instance there is a set of questions that have to be answered, and this is something that can be simplified."
"The tool must provide proper guidelines to troubleshoot connectivity issues."
"The licensing cost is expensive."
"Apache Spark provides very good performance The tuning phase is still tricky."
"Apache Spark should add some resource management improvements to the algorithms."
"The logging for the observability platform could be better."
"There were some problems related to the product's compatibility with a few Python libraries."
"If you have a Spark session in the background, sometimes it's very hard to kill these sessions because of D allocation."
"Apache Spark could improve the connectors that it supports. There are a lot of open-source databases in the market. For example, cloud databases, such as Redshift, Snowflake, and Synapse. Apache Spark should have connectors present to connect to these databases. There are a lot of workarounds required to connect to those databases, but it should have inbuilt connectors."
"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."
"Spark could be improved by adding support for other open-source storage layers than Delta Lake."
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.
