We performed a comparison between Apache Spark and IBM Spectrum Computing based on real PeerSpot user reviews.
Find out in this report how the two Hadoop solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."The deployment of the product 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."
"The product’s most valuable features are lazy evaluation and workload distribution."
"Spark can handle small to huge data and is suitable for any size of company."
"The most crucial feature for us is the streaming capability. It serves as a fundamental aspect that allows us to exert control over our operations."
"There's a lot of functionality."
"It's easy to prepare parallelism in Spark, run the solution with specific parameters, and get good performance."
"I like that it can handle multiple tasks parallelly. I also like the automation feature. JavaScript also helps with the parallel streaming of the library."
"Spectrum Computing's best features are its speed, robustness, and data processing and analysis."
"We are satisfied with the technical support, we have no issues."
"Easy to operate and use."
"This solution is working for both VTL and tape."
"The most valuable feature is the backup capability."
"The most valuable aspect of the product is the policy driving resource management, to optimize the computing across data centers."
"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."
"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."
"It requires overcoming a significant learning curve due to its robust and feature-rich nature."
"The logging for the observability platform could be better."
"When using Spark, users may need to write their own parallelization logic, which requires additional effort and expertise."
"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."
"Apart from the restrictions that come with its in-memory implementation. It has been improved significantly up to version 3.0, which is currently in use."
"The graphical user interface (UI) could be a bit more clear. It's very hard to figure out the execution logs and understand how long it takes to send everything. If an execution is lost, it's not so easy to understand why or where it went. I have to manually drill down on the data processes which takes a lot of time. Maybe there could be like a metrics monitor, or maybe the whole log analysis could be improved to make it easier to understand and navigate."
"Lack of sufficient documentation, particularly in Spanish."
"SMB storage and HPC is not compatible and it should be supported by IBM Spectrum Computing."
"We have not been able to use deduplication."
"Spectrum Computing is lagging behind other products, most likely because it hasn't been shifted to the cloud."
"We'd like to see some AI model training for machine learning."
"This solution is no longer managing tapes correctly."
Apache Spark is ranked 1st in Hadoop with 60 reviews while IBM Spectrum Computing is ranked 7th in Hadoop with 6 reviews. Apache Spark is rated 8.4, while IBM Spectrum Computing is rated 7.8. 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 IBM Spectrum Computing writes "Provides stable backup for our databases and has good technical support ". Apache Spark is most compared with Spring Boot, AWS Batch, Spark SQL, SAP HANA and Cloudera Distribution for Hadoop, whereas IBM Spectrum Computing is most compared with HPE Ezmeral Data Fabric and IBM Turbonomic. See our Apache Spark vs. IBM Spectrum Computing report.
See our list of best Hadoop vendors.
We monitor all Hadoop 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.
