Kentik - Network Flow Analytics

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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

Leveraging Big Data for Continuous Improvement

May 31, 2016

Intelligent use of network management data can enable virtually any company to transform itself into a successful digital business. In our third post in this series, we look at areas where traditional network data management approaches are falling short, and we consider how a Big Data platform that provides real-time answers to ad-hoc queries can empower IT organizations and drive continuous improvement in both business and IT operations.

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Peering for the Win

May 23, 2016

Traffic can get from anywhere to anywhere on the Internet, but that doesn’t mean all networks are directly connected. Instead, each network operator chooses the networks with which to connect. Both business and technical considerations are involved, and the ability to identify prime candidates for peering or transit offers significant competitive advantages. In this post we look at the benefits of intelligent interconnects and how networks can find the best peers to connect with.

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Kentik APIs for Customer Portal Integration

May 16, 2016

The network data collected by Kentik Detect isn’t limited to portal-only access; it can also be queried via SQL client or using Kentik’s RESTful APIs. In this how-to, we look how service providers can use our Data Explorer API to integrate traffic graphs into a customer portal, creating added-value content that can differentiate a provider from its competitors while keeping customers committed and engaged.

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Transforming NetOps with Big Data

May 08, 2016

Looking ahead to tomorrow’s economy, today’s savvy companies are transitioning into the world of digital business. In this post — the second of a three-part series — guest contributor Jim Metzler examines the key role that Big Data can play in that transformation. By revolutionizing how operations teams collect, store, access, and analyze network data, a Big Data approach to network management enables the agility that companies will need to adapt and thrive.

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Inside the Kentik Data Engine, Part 2

May 02, 2016

In part 2 of our tour of Kentik Data Engine, the distributed backend that powers Kentik Detect, we continue our look at some of the key features that enable extraordinarily fast response to ad hoc queries even over huge volumes of data. Querying KDE directly in SQL, we use actual query results to quantify the speed of KDE’s results while also showing the depth of the insights that Kentik Detect can provide.

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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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NetFlow, sFlow, and Flow Extensibility, Part 2

April 18, 2016

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

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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Maximizing Network Metadata Value

March 21, 2016

The plummeting cost of storage and CPU allows us to apply distributed computing technology to network visibility, enabling long-term retention and fast ad hoc querying of metadata. In this post we look at what network metadata actually is and how its applications for everyday network operations — and its benefits for business — are distinct from the national security uses that make the news.

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