Find out why data stream processing works better — faster and more efficiently — on Quix, compared to industry incumbents Apache Spark and Apache Flink.
Results: How three client libraries handle stream processing at scale
See detailed performance metrics and code behind stream processing tasks executed on Quix, Spark and Flink. We tested performance, efficiency, ease of use and scalability.
A (very) detailed comparison of Quix, Spark and Flink
To successfully use stream processing, select the best architecture for your business. This report offers an exhaustive comparison of the pros and cons for each client library to help you make the most of your streaming data.
Our experience testing each client library
Go behind the scenes with one developer as we set up and test Quix, Spark and Flink. A key question is usability, because time wasted wrangling complex infrastructure means less time building great data-driven products.
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Why the data pipeline is changing everything
Find out why analysts and market-watchers agree that traditional data processing must be replaced by stream processing. It’s happening now as innovators embrace new opportunities for greater personalization, automation and revenue.
Fill out the form to receive the white paper to your inbox.
Find out why analysts and market-watchers agree that traditional data processing must be replaced by stream processing. It’s happening now as innovators embrace new opportunities for greater personalization, automation and revenue.
Fill out the form to receive the white paper to your inbox.
