Kentik - Network Observability

Kentik Blog: How-To

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by Avi Freedman
by Avi Freedman
by Avi Freedman
by Avi Freedman
by Avi Freedman
by Avi Freedman
by Aaron Kagawa, Crystal Li
by Ken Osowski
by Crystal Li, Jim Meehan
by Ken Osowski
by Jim Meehan, Greg Villain
by Crystal Li

Using NetFlow Analysis to Optimize IP Transit

January 9, 2017

Unless you’re a Tier 1 provider, IP transit is a significant cost of providing Internet service or operating a digital business. To minimize the pain, your network monitoring tools would ideally show you historical route utilization and notify you before the traffic volume on any path triggers added fees. In this post we look at how Kentik Detect is able to do just that, and we show how our Data Explorer is used to drill down on the details of route utilization.

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Kentik Troubleshoots Network Performance

December 5, 2016

How does Kentik NPM help you track down network performance issues? In this post by Jim Meehan, Director of Solutions Engineering, we look at how we recently used our own NPM solution to determine if a spike in retransmits was due to network issues or a software update we’d made on our application servers. You’ll see how we ruled out the software update, and were then able to narrow the source of the issue to a specific route using BGP AS Path.

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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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Using Kentik Detect to Find Current Attacks

December 15, 2015

With massive data capacity and analytical flexibility, Kentik Detect makes it easy to actively explore network traffic. In this post we look at how to use this capability to rapidly discover and analyze interesting and potentially important DDoS and other attack vectors. We start with filtering by source geo, then zoom in on a time-span with anomalous traffic. By looking at unique source IPs and grouping traffic by destination IP we find both the source and the target of an attack.

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