We performed a comparison between Apache Spark and AWS Compute Optimizer based on real PeerSpot user reviews.
Find out what your peers are saying about Amazon Web Services (AWS), Apache, Zadara and others in Compute Service."The solution has been very stable."
"Its scalability and speed are very valuable. You can scale it a lot. It is a great technology for big data. It is definitely better than a lot of earlier warehouse or pipeline solutions, such as Informatica. Spark SQL is very compliant with normal SQL that we have been using over the years. This makes it easy to code in Spark. It is just like using normal SQL. You can use the APIs of Spark or you can directly write SQL code and run it. This is something that I feel is useful in Spark."
"Apache Spark provides a very high-quality implementation of distributed data processing."
"I feel the streaming is its best feature."
"The fault tolerant feature is provided."
"The product is useful for analytics."
"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."
"The main feature that we find valuable is that it is very fast."
"I find the solution's scaling capability to be an important benefit. You can scale it vertically or horizontally, i.e., you can upgrade the hardware or clone the machine. The solution is also easy to manage and flexible. Additionally, you get some layers of security without paying for it."
"The solution’s integration with other platforms should be improved."
"This solution currently cannot support or distribute neural network related models, or deep learning related algorithms. We would like this functionality to be developed."
"More ML based algorithms should be added to it, to make it algorithmic-rich for developers."
"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."
"Apache Spark is very difficult to use. It would require a data engineer. It is not available for every engineer today because they need to understand the different concepts of Spark, which is very, very difficult and it is not easy to learn."
"If you have a Spark session in the background, sometimes it's very hard to kill these sessions because of D allocation."
"Dynamic DataFrame options are not yet available."
"Spark could be improved by adding support for other open-source storage layers than Delta Lake."
"I have two areas of improvement to comment on. Most of the product names in AWS are not indicative of what they are doing. Moreover, AWS is not organized and you do not have the full platform with you. It is hard to know some AWS services."
Apache Spark is ranked 5th in Compute Service with 60 reviews while AWS Compute Optimizer is ranked 10th in Compute Service with 1 review. Apache Spark is rated 8.4, while AWS Compute Optimizer is rated 8.0. 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 Compute Optimizer writes "Easy to manage, flexible, and has good scaling options". Apache Spark is most compared with Spring Boot, AWS Batch, Spark SQL, SAP HANA and Cloudera Distribution for Hadoop, whereas AWS Compute Optimizer is most compared with .
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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.
