We performed a comparison between Apache Spark and AWS Lambda 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."Spark can handle small to huge data and is suitable for any size of company."
"Apache Spark provides a very high-quality implementation of distributed data processing."
"With Spark, we parallelize our operations, efficiently accessing both historical and real-time data."
"The memory processing engine is the solution's most valuable aspect. It processes everything extremely fast, and it's in the cluster itself. It acts as a memory engine and is very effective in processing data correctly."
"The processing time is very much improved over the data warehouse solution that we were using."
"The most valuable feature is the Fault Tolerance and easy binding with other processes like Machine Learning, graph analytics."
"ETL and streaming capabilities."
"There's a lot of functionality."
"The stability is good."
"Technical support has been great in general."
"You can spin up anything instantly without any investment."
"The installation and configuration of the solution is straightforward."
"AWS Lambda's best features are log analysis and event triggering and actioning."
"We have no issues with the technical support."
"The support from AWS Lambda is very good, they are responsive."
"Amazon takes care of the scalability. That's the right way. It's automatic and it's fully managed. That's one benefit of Lambda."
"Spark could be improved by adding support for other open-source storage layers than Delta Lake."
"Needs to provide an internal schedule to schedule spark jobs with monitoring capability."
"Apache Spark provides very good performance The tuning phase is still tricky."
"They could improve the issues related to programming language for the platform."
"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."
"We are building our own queries on Spark, and it can be improved in terms of query handling."
"Apache Spark can improve the use case scenarios from the website. There is not any information on how you can use the solution across the relational databases toward multiple databases."
"The initial setup was not easy."
"AWS Lambda has some size limitations in the code line, you can't do a couple of functions to do the job."
"My opinion is that the integration could be improved in this solution. We have had some difficulties integrating the EC2 module, but we found a solution for that by ourselves."
"AWS Lambda's GUI could be improved with a twist or tweak in its look and feel to make it more impressive."
"The tool changes its UI every month which is very frustrating for me. I don’t know why AWS keeps changing the UI. They can’t stick to a specific one"
"Amazon doesn't have enough local support based in our country."
"AWS Lambda should support additional languages."
"If you're running a new application with a significant load, you need to be prepared for potential bottlenecks."
"Lambda could be improved in the sense that some of the things done with Lambda function take some time. So the performance could be better and faster."
Apache Spark is ranked 5th in Compute Service with 60 reviews while AWS Lambda is ranked 1st in Compute Service with 70 reviews. Apache Spark is rated 8.4, while AWS Lambda is rated 8.6. The top reviewer of Apache Spark writes "Reliable, able to expand, and handle large amounts of data well". On the other hand, the top reviewer of AWS Lambda writes "An easily scalable solution with a variety of use cases and valuable event-based triggers". Apache Spark is most compared with Spring Boot, AWS Batch, Spark SQL, SAP HANA and Apache NiFi, whereas AWS Lambda is most compared with AWS Batch, Amazon EC2 Auto Scaling, Apache NiFi, AWS Fargate and Google Cloud Dataflow. See our AWS Lambda 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.
