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Month: April 2016

Inside the Kentik Data Engine, Part 1

By Jim Meehan, Director of Product Marketing | Apr 25, 2016
How-To, Network Management

Kentik Detect’s backend is Kentik Data Engine (KDE), a distributed datastore that’s architected to ingest IP flow records and related network data at backbone scale and to execute exceedingly fast ad-hoc queries over very large datasets, making it optimal for both real-time and historical analysis of network traffic. In this series, we take a tour of KDE, using standard Postgres CLI query syntax to explore and quantify a variety of performance and scale characteristics.

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NetFlow, sFlow, and Flow Extensibility, Part 2

By Avi Freedman, Co-founder & CEO | Apr 18, 2016
Network Management, Technology

NetFlow and IPFIX use templates to extend the range of data types that can be represented in flow records. sFlow addresses some of the downsides of templating, but in so doing takes away the flexibility that templating allows. In this post we look at the pros and cons of sFlow, and consider what the characteristics might be of a solution can support templating without the shortcomings of current template-based protocols.

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Beyond Hadoop

By Jim Meehan, Director of Product Marketing | Apr 11, 2016
Network Management, Technology

As the first widely accessible distributed-computing platform for large datasets, Hadoop is great for batch processing data. But when you need real-time answers to questions that can’t be fully defined in advance, the MapReduce architecture doesn’t scale. In this post we look at where Hadoop falls short, and we explore newer approaches to distributed computing that can deliver the scale and speed required for network analytics.

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