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 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 found the solution stable. We haven't had any problems with it."
"The product’s most valuable features are lazy evaluation and workload distribution."
"The product’s most valuable feature is the SQL tool. It enables us to create a database and publish it."
"The product's deployment phase is easy."
"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."
"We use it for ETL purposes as well as for implementing the full transformation pipelines."
"One of Apache Spark's most valuable features is that it supports in-memory processing, the execution of jobs compared to traditional tools is very fast."
"The most valuable feature of this solution is the API Gateway."
"Thanks to this solution, we do not need to worry about hardware or resource utilization. It saves us time."
"It is my preferred product, as it provides me with source code within the solution."
"AWS Lambda has improved our productivity and functionality."
"Provides a good, easy path from when you're using an AWS cluster."
"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."
"AWS Lambda's best features are log analysis and event triggering and actioning."
"The main features of this solution are the ability to integrate multiple AWS applications or external applications very quickly and organize all of them. Additionally, it is easy to use and you can run various programming languages, such as Python, Go, and Java."
"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."
"We are building our own queries on Spark, and it can be improved in terms of query handling."
"There were some problems related to the product's compatibility with a few Python libraries."
"It should support more programming languages."
"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."
"They could improve the issues related to programming language for the platform."
"Apache Spark should add some resource management improvements to the algorithms."
"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."
"I want to see support for longer applications. I need the 15-minute time-out window to improve."
"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."
"We need to better understand Lambda for different scenarios. We need some joint effort between Amazon and the users to have the users identify how they can really leverage Lambda. It's not about Lambda itself; it's about the practice, the guidance. There needs to be very good documentation. From the user perspective, what exists now is not always enough."
"The solution should continue to streamline integrations with AWS services."
"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"
"The running time of AWS Lambda runs fine. It takes around five minutes but it would be great if that time could be extended."
"AWS Lambda should support additional languages."
"I would like to see the five zero four AWS Lambda invocation fixed. This is basically a time-out error."
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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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.
