Kentik - Network Flow Analytics

Posts by Jim Meehan

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About Jim Meehan

Jim Meehan is the director of product marketing at Kentik. For more than two decades, he has worked as a security and networking technologist in a variety of roles, including founder, technical sales leader, network architect, and software developer. Prior to Kentik, Jim designed infrastructure for several Silicon Valley startups, and delivered Arbor Networks’ enterprise security, network visibility and DDoS mitigation products to a who’s-who of online enterprises. Before that, he co-founded one of the first dial-up ISPs in the Chicago area and founded a DSL provider that beat AT&T to market in much of NW metro Chicago — all while still in high school. As a volunteer, he has written thousands of lines of custom code for Drupal and CiviCRM at Bay Area Children’s Theatre, where his wife is co-founder and executive director. He’s also dad to three children, two boys and a girl.

Disney+ Launch: What Broadband Providers Need to See

November 18, 2019

Disney+ launched with an impressive debut, especially considering that technical problems, potentially capacity issues, plagued the service shortly after launch. In this post, we look at Disney+ traffic peaks around launch. We also explain why, during high-profile launches like that of Disney+, broadband subscribers are likely to blame their provider for any perceived problems. Network operators must be able to understand the dynamics of new loads placed on the network by new services and plan accordingly.

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Level 3 Route Leak: What Kentik Saw

November 15, 2017

As last week’s misconfigured BGP routes from backbone provider Level 3 caused Internet outages across the nation, the monitoring and troubleshooting capabilities of Kentik Detect enabled us to identify the most-affected providers and assess the performance impact on our own customers. In this post we show how we did it and how our new ability to alert on performance metrics will make it even easier for Kentik customers to respond rapidly to similar incidents in the future.

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Kentik for Site Reliability

October 30, 2017

At Kentik, we built Kentik Detect, our production SaaS platform, on a microservices architecture. We also use Kentik for monitoring our own infrastructure. Drawing on a variety of real-life incidents as examples, this post looks at how the alerts we get — and the details that we’re able to see when we drill down deep into the data — enable us to rapidly troubleshoot and resolve network-related issues.

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Visualizing the Digital Eclipse

August 22, 2017

With much of the country looking skyward during the solar eclipse, you might wonder how much of an effect there was on network traffic. Was there a drastic drop as millions of watchers were briefly uncoupled from their screens? Or was that offset by a massive jump in live streaming and photo uploads? In this post we report on what we found using forensic analytics in Kentik Detect to slice traffic based on how and where usage patterns changed during the event.

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Accuracy in Low-Volume NetFlow Sampling

June 19, 2017

NetFlow data has a lot to tell you about the traffic across your network, but it may require significant resources to collect. That’s why many network managers choose to collect flow data on a sampled subset of total traffic. In this post we look at some testing we did here at Kentik to determine if sampling prevents us from seeing low-volume traffic flows in the context of high overall traffic volume.

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SDN and Self-Driving Networks

May 15, 2017

SDN holds lots of promise, but it’s practical applications have so far been limited to discrete use cases like customer provisioning or service scaling. The long-term goal is true dynamic control, but that requires comprehensive traffic intelligence in real time at full scale. As our customers are discovering, Kentik Detect’s traffic visibility, anomaly detection, and extensive APIs make it an ideal source for actionable traffic data that can drive network automation.

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Package Tracking for the Internet

May 9, 2017

Without package tracking, FedEx wouldn’t know how directly a package got to its destination or how to improve service and efficiency. 25 years into the commercial Internet, most service providers find themselves in just that situation, with no easy way to tell where an individual customer’s traffic exited the network. With Kentik Detect’s new Ultimate Exit feature, those days are over. Learn how Kentik’s per-customer traffic breakdown gives providers a competitive edge.

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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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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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Inside the Kentik Data Engine, Part 2

May 2, 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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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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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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Detecting Hidden Spambots

December 3, 2015

If your network visibility tool lets you query only those flow details that you’ve specified in advance then you’re likely vulnerable to threats that you haven’t anticipated. In this post we’ll explore how SQL querying of Kentik Detect’s unified, full-resolution datastore enables you to drill into traffic anomalies, to identify threats, and to define alerts that notify you when similar issues recur.

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