Kentik Blog

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Inside the Kentik Data Engine, Part 1

April 25, 2016

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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Beyond Hadoop

April 11, 2016

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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Evolution of BGP NetFlow Analysis, Part 2

March 14, 2016

In part 2 of this series, we look at how Big Data in the cloud enables network visibility solutions to finally take full advantage of NetFlow and BGP. Without the constraints of legacy architectures, network data (flow, path, and geo) can be unified and queries covering billions of records can return results in seconds. Meanwhile the centrality of networks to nearly all operations makes state-of-the-art visibility essential for businesses to thrive.

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