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."It provides a scalable machine learning library."
"The memory processing engine is the solution's most valuable aspect. It processes everything extremely fast, and it's in the cluster itself. It acts as a memory engine and is very effective in processing data correctly."
"The most valuable feature of this solution is its capacity for processing large amounts of data."
"The product’s most valuable features are lazy evaluation and workload distribution."
"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 data processing framework is good."
"It is highly scalable, allowing you to efficiently work with extensive datasets that might be problematic to handle using traditional tools that are memory-constrained."
"We use it for ETL purposes as well as for implementing the full transformation pipelines."
"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 initial setup was not easy."
"This solution currently cannot support or distribute neural network related models, or deep learning related algorithms. We would like this functionality to be developed."
"The management tools could use improvement. Some of the debugging tools need some work as well. They need to be more descriptive."
"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."
"At times during the deployment process, the tool goes down, making it look less robust. To take care of the issues in the deployment process, users need to do manual interventions occasionally."
"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."
"At the initial stage, the product provides no container logs to check the activity."
"It would be beneficial to enhance Spark's capabilities by incorporating models that utilize features not traditionally present in its framework."
"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.
