In the last decade, many systems for the extraction of operational statistics from computer network interconnects have been designed and implemented. Those systems generate huge amounts of data of various formats and in various granularities, from packet level to statistics about whole flows. In addition, the complexity of Internet services has increased drastically with the introduction of cloud infrastructures, Content Delivery Networks (CDNs) and mobile Internet usage, and complexity will continue to increase in the future with the rise of Machine-to-Machine communication and ubiquitous wearable devices. Therefore, current and future network monitoring frameworks cannot rely only on information gathered at a single network interconnect, but must consolidate information from various vantage points distributed across the network. In this paper, we present DBStream, a holistic approach to large-scale network monitoring and analysis applications. After a precise system introduction, we show how its Continuous Execution Language (CEL) can be used to automate several data processing and analysis tasks typical for monitoring operational ISP networks. We discuss the performance of DBStream as compared to MapReduce processing engines and show how intelligent job scheduling can increase its performance even further. Furthermore, we show the versatility of DBStream by explaining how it has been integrated to import and process data from two passive network monitoring systems, namely METAWIN and Tstat. Finally, multiple examples of network monitoring applications are given, ranging from simple statistical analysis to more complex traffic classification tasks applying machine learning techniques using the Weka toolkit.

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