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 product’s most valuable feature is the SQL tool. It enables us to create a database and publish it."
"The most valuable feature of Apache Spark is its memory processing because it processes data over RAM rather than disk, which is much more efficient and fast."
"The most valuable feature of this solution is its capacity for processing large amounts of data."
"The deployment of the product is easy."
"It's easy to prepare parallelism in Spark, run the solution with specific parameters, and get good performance."
"The product's deployment phase is easy."
"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."
"It is a scalable solution."
"The solution is highly scalable."
"Lambda is the preferred compute option because of on-demand cost. We don't have to provision any hardware beforehand. We don't have to provision the capacity required for the services because it is serverless."
"Provides a good, easy path from when you're using an AWS cluster."
"One of the most valuable features of AWS Lambda is the performance. Lambda is very technical and has very high performance, as well as good real-time performance."
"Because AWS Lambda is serverless, server configuration is not required, and we can run it directly anywhere."
"AWS Lambda has improved our productivity and functionality."
"You can spin up anything instantly without any investment."
"One limitation is that not all machine learning libraries and models support it."
"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."
"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."
"Needs to provide an internal schedule to schedule spark jobs with monitoring capability."
"The product could improve the user interface and make it easier for new users."
"Include more machine learning algorithms and the ability to handle streaming of data versus micro batch processing."
"This solution currently cannot support or distribute neural network related models, or deep learning related algorithms. We would like this functionality to be developed."
"The setup I worked on was really complex."
"AWS Lambda can improve its file system-based sharing capabilities and restrictions."
"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."
"It could be cheaper."
"Another challenge I've noticed is that there is a limit to the environment variables such as the 4 KB limit. Although, the advice is to use parameters or other things to store the details when the limit has exceeded the data, this adds additional intensity to the application. If the size limits for environment variables can be revealed, it would be helpful. Even if we have to pay for it, at least we would know that we are not dealing with latency. So, I would like to see the size of the environment variables increased."
"We don't have the inbuilt modules in AWS Lambda. If more modules were built into or integrated with AWS Lambda, that would help developers to code."
"The support team does not know how to implement and build the solution."
"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."
"The setup was pretty complex because there were many steps. For me, it was complex because I was somewhat new at it. It could be easier for someone who has done it a bunch of times. I just found that it was a very dense user experience. There's a lot going on during setup."
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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