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 processing time is very much improved over the data warehouse solution that we were using."
"The solution is very stable."
"The scalability has been the most valuable aspect of the solution."
"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."
"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."
"With Hadoop-related technologies, we can distribute the workload with multiple commodity hardware."
"Provides a lot of good documentation compared to other solutions."
"With Spark, we parallelize our operations, efficiently accessing both historical and real-time data."
"The support from AWS Lambda is very good, they are responsive."
"I like the pay-for-what-you-use feature. This is the main reason why we use AWS Lambda. I don't have to manage servers; I just have to configure Lambda and expose it to an API gateway."
"The basic feature that I like is that there is no server installation. It also has good support for various languages, such as Java, .NET, C#, and Python."
"The stability is good."
"AWS Lambda has improved our productivity and functionality."
"AWS Lambda is serverless."
"The ability to scale up and down very quickly helps because we can maintain our system performance and business at a low cost."
"The ease and speed of developing the services using any available language is the most valuable feature."
"They could improve the issues related to programming language for the platform."
"We've had problems using a Python process to try to access something in a large volume of data. It crashes if somebody gives me the wrong code because it cannot handle a large volume of data."
"Technical expertise from an engineer is required to deploy and run high-tech tools, like Informatica, on Apache Spark, making it an area where improvements are required to make the process easier for users."
"Needs to provide an internal schedule to schedule spark jobs with monitoring capability."
"The management tools could use improvement. Some of the debugging tools need some work as well. They need to be more descriptive."
"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."
"There could be enhancements in optimization techniques, as there are some limitations in this area that could be addressed to further refine Spark's performance."
"Its UI can be better. Maintaining the history server is a little cumbersome, and it should be improved. I had issues while looking at the historical tags, which sometimes created problems. You have to separately create a history server and run it. Such things can be made easier. Instead of separately installing the history server, it can be made a part of the whole setup so that whenever you set it up, it becomes available."
"I want to see support for longer applications. I need the 15-minute time-out window to improve."
"AWS Lambda could be improved by increasing the size of the payload. Also, sometimes Lambda doesn't implement well for bigger solutions."
"The user-friendliness of the solution could be improved."
"Lambda can only be used in one account; there's no possibility to utilize it in another account."
"I would like to see some better integration with other providers, like Cohesity, Druva, and others. I also think the Lambda interface could be better."
"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."
"AWS Lambda could improve by having no-code or low-code options because currently, you need to be able to write code well to use it."
"There's room for improvement in the testing 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 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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