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 features we find most valuable are the machine learning, data learning, and Spark Analytics."
"Now, when we're tackling sentiment analysis using NLP technologies, we deal with unstructured data—customer chats, feedback on promotions or demos, and even media like images, audio, and video files. For processing such data, we rely on PySpark. Beneath the surface, Spark functions as a compute engine with in-memory processing capabilities, enhancing performance through features like broadcasting and caching. It's become a crucial tool, widely adopted by 90% of companies for a decade or more."
"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."
"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."
"Features include machine learning, real time streaming, and data processing."
"Spark helps us reduce startup time for our customers and gives a very high ROI in the medium term."
"The fault tolerant feature is provided."
"The main feature that we find valuable is that it is very fast."
"AWS Lambda is itself serverless, and it is connected to the API gateway, and you can directly call the API through the API gateway and connect through AWS Lambda."
"Lambda has improved our organization by making it possible to transform data."
"By using Lambda, we can use Python code and the Boto3 solution."
"Lambda makes the administration of all our services related to Amazon really easy."
"I have found all of the features valuable. It's an easy and cheap solution."
"Provides a good, easy path from when you're using an AWS cluster."
"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."
"It's a serverless solution which is the best feature. It helps us because it offers free aspects. From the infrastructure perspective, it helps us manage costs. There is no overhead of estimating how much infrastructure we're going to need. We can focus on building the business functionality that we want to build."
"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."
"Apache Spark could potentially improve in terms of user-friendliness, particularly for individuals with a SQL background. While it's suitable for those with programming knowledge, making it more accessible to those without extensive programming skills could be beneficial."
"When you are working with large, complex tasks, the garbage collection process is slow and affects performance."
"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."
"Include more machine learning algorithms and the ability to handle streaming of data versus micro batch processing."
"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."
"Spark could be improved by adding support for other open-source storage layers than Delta Lake."
"There were some problems related to the product's compatibility with a few Python libraries."
"Amazon doesn't have enough local support based in our country."
"They should work on the solution's stability and pricing."
"The price in general could always be better."
"Security needs to be improved."
"What could be improved in AWS Lambda is a tricky question because I base the area for improvement on a specific matrix, for example, latency, so I'm still determining if I can be the judge on that. However, room for improvement could be when you're using AWS Lambda as a backend, it can be challenging to use it for monitoring. Monitoring is critical in development, and I don't have much expertise in the area, but you can use other services such as Xray. I found that monitoring on AWS Lambda is a challenge. The tool needs better monitoring. Another area for improvement in AWS Lambda is the cold start, where it takes some time to invoke a function the first time, but after that, invoking it becomes swift. Still, there's room for improvement in that AWS Lambda process. In the next release of AWS Lambda, I'd like AWS to improve monitoring so that I can monitor codes better."
"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."
"Lambda has limitations on the amount of memory you can use and is not a good solution for long running processes."
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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