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."The solution is very stable."
"The most crucial feature for us is the streaming capability. It serves as a fundamental aspect that allows us to exert control over our operations."
"AI libraries are the most valuable. They provide extensibility and usability. Spark has a lot of connectors, which is a very important and useful feature for AI. You need to connect a lot of points for AI, and you have to get data from those systems. Connectors are very wide in Spark. With a Spark cluster, you can get fast results, especially for AI."
"The most valuable feature is the Fault Tolerance and easy binding with other processes like Machine Learning, graph analytics."
"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."
"The data processing framework is good."
"The solution has been very stable."
"The processing time is very much improved over the data warehouse solution that we were using."
"The ease and speed of developing the services using any available language is the most valuable feature."
"The most valuable feature of AWS Lambda is that you can trigger and run jobs instantly, and after you complete the job, that function is either destroyed or stopped automatedly."
"AWS Lambda is a stable solution."
"We are building a Twitter-like application in the boot camp. I have used Lamda for the integration of the post-confirmation page in the application. This will help you get your one-time password via mail. You can log in with the help of a post-confirmation page. We didn’t want to setup an instance specifically for confirmation. We used the Lambda function so that it goes back to sleep after pushing up."
"The most valuable features of AWS Lambda are a serverless and event-driven architecture."
"The solution is designed very well. You don't need to keep a server up. You just need some router to route your API request and Lambda provides a very well-designed feature to process the request."
"It is a scalable solution."
"The automation feature is valuable."
"When you first start using this solution, it is common to run into memory errors when you are dealing with large amounts of data."
"Needs to provide an internal schedule to schedule spark jobs with monitoring capability."
"They could improve the issues related to programming language for the platform."
"At times during the deployment process, the tool goes down, making it look less robust. To take care of the issues in the deployment process, users need to do manual interventions occasionally."
"The migration of data between different versions could be improved."
"We are building our own queries on Spark, and it can be improved in terms of query handling."
"One limitation is that not all machine learning libraries and models support it."
"In data analysis, you need to take real-time data from different data sources. You need to process this in a subsecond, do the transformation in a subsecond, and all that."
"The product could make the process of integration easier."
"Lambda's dashboard could be more user-friendly and customizable. I want the dashboard to have more information to quickly identify what functions and events are running. Also, we want to be able to add more trigger points, push notifications, and events."
"There are other similar solutions, such as Google Cloud Platform or Microsoft Azure. They might be better for small tasks."
"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"
"My engineers work with it on a daily basis. I just don't have enough depth of knowledge about what kinds of edge cases they may have tried and found lacking. There may be some issues with some language support at one point or another because we couldn't get the underlying libraries in there. A lot of what we do is either in JavaScript, Python, or some of the non-compiled languages. I'm not sure if we've ever tried building a C# solution, for instance, in Lambda or a Java solution in Lambda. It doesn't mean those aren't its capabilities. I would rather refer to my engineers for where the boundaries are."
"We'd love to see more integration potential in the future."
"AWS Lambda needs to improve its stability."
"The feature to attach external storage, such as an S3 or elastic storage, must be added to AWS Lambda. This is its area for improvement."
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 Azure Stream Analytics, 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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