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."We use Spark to process data from different data sources."
"The most valuable feature of this solution is its capacity for processing large amounts of data."
"The product’s most valuable feature is the SQL tool. It enables us to create a database and publish it."
"One of Apache Spark's most valuable features is that it supports in-memory processing, the execution of jobs compared to traditional tools is very fast."
"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."
"Provides a lot of good documentation compared to other solutions."
"Features include machine learning, real time streaming, and data processing."
"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."
"Easy to operate and use."
"The most valuable feature is the backup capability."
"We are satisfied with the technical support, we have no issues."
"Spectrum Computing's best features are its speed, robustness, and data processing and analysis."
"This solution is working for both VTL and tape."
"The most valuable aspect of the product is the policy driving resource management, to optimize the computing across data centers."
"Stream processing needs to be developed more in Spark. I have used Flink previously. Flink is better than Spark at stream processing."
"The logging for the observability platform could be better."
"It's not easy to install."
"When using Spark, users may need to write their own parallelization logic, which requires additional effort and expertise."
"The product could improve the user interface and make it easier for new users."
"There were some problems related to the product's compatibility with a few Python libraries."
"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."
"If you have a Spark session in the background, sometimes it's very hard to kill these sessions because of D allocation."
"Spectrum Computing is lagging behind other products, most likely because it hasn't been shifted to the cloud."
"We have not been able to use deduplication."
"This solution is no longer managing tapes correctly."
"Lack of sufficient documentation, particularly in Spanish."
"We'd like to see some AI model training for machine learning."
"SMB storage and HPC is not compatible and it should be supported by IBM Spectrum Computing."
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.
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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.
