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."
"It's easy to prepare parallelism in Spark, run the solution with specific parameters, and get good performance."
"We use it for ETL purposes as well as for implementing the full transformation pipelines."
"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."
"There's a lot of functionality."
"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 Apache Spark is its ease of use."
"The scalability has been the most valuable aspect of the solution."
"The installation and configuration of the solution is straightforward."
"Provides a good, easy path from when you're using an AWS cluster."
"The solution integrates well with API gateways and S3 events via its AWS ecosystem."
"We have no issues with the technical support."
"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."
"Technical support has been great in general."
"You can spin up anything instantly without any investment."
"It enables the launch of thousands of instances simultaneously,"
"More ML based algorithms should be added to it, to make it algorithmic-rich for developers."
"One limitation is that not all machine learning libraries and models support it."
"Stability in terms of API (things were difficult, when transitioning from RDD to DataFrames, then to DataSet)."
"Stream processing needs to be developed more in Spark. I have used Flink previously. Flink is better than Spark at stream processing."
"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."
"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."
"We use big data manager but we cannot use it as conditional data so whenever we're trying to fetch the data, it takes a bit of time."
"The setup I worked on was really complex."
"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."
"AWS Lambda's GUI could be improved with a twist or tweak in its look and feel to make it more impressive."
"If it is a specific ETL process or a long-term one, then AWS Lambda is not a good option."
"We've had to revamp the way that it works due to that 15-minute timeout limitation."
"Lamba functions have cold-starts that can cause some delay."
"I would like to see the five zero four AWS Lambda invocation fixed. This is basically a time-out error."
"There's room for improvement in the testing setup."
"I think that perhaps Lambda could explore its functionality more."
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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