Our network analytics platform supports visibility within public cloud environments via VPC Flow Logs. Our initial integration used VPC Flow Logs from Google Cloud Platform. Today, we are excited to extend our support to AWS. Read how we do it in this blog post.
During Networking Field Day 19, Kentik presented on new capabilities for service providers, cloud and cloud-native environments, and gave a technical talk on tagging and data enrichment. In this post, we recap the event highlights and provide the videos for watching and sharing.
We post a lot on our blog about our advanced network analytics platform, use cases, and the ROI we deliver to service providers and enterprises globally. However, today’s post is for our fellow programmers, as we go under Kentik’s hood to discuss Rust.
Real-time network data insights are not only important to the service provider. In this post, we discuss why service providers’ end-customers, who consume those services — subscribers, digital enterprise, hosting customers, etc., also need visibility.
Relentless traffic growth and a constant stream of new technologies, e.g. SDN and cloud interconnects, make it harder to understand how services traverse the network between application infrastructure and users or customers. In this post, we discuss how that led Kentik to build our BGP Ultimate Exit to help address traffic visibility challenges.
Service assurance and incident response are just one side of the network performance coin. What if you could use the same data to provide additional value to customers, and highlight the great service you provide? Today we announced the “My Kentik” portal to do just that. Read the details in this post.
We just published our first open source project on GitHub and npm. Called Mobx Form, in this blog post we look at how the project helps developers with coding complex forms.
As security threats grow more ominous, security procedures grow more onerous, which can be a drag on productivity. In this post we look at how Kentik’s single sign-on (SSO) implementation enables users to maintain security without constantly entering authentication credentials. Check out this walk-through of the SSO setup and login process to enable your users to access Kentik Detect with the same SSO services they use for other applications.
In our latest post on Interface Classification, we look beyond what it is and how it works to why it’s useful, illustrated with a few use cases that demonstrate its practical value. By segmenting traffic based on interface characteristics (Connectivity Type and Network Boundary), you’ll be able to easily see and export valuable intelligence related to the cost and ROI of carrying a given customer’s traffic.
Kentik addresses the day-to-day challenges of network operations, but our unique big network data platform also generates valuable business insights. A great example of this duality is our new Interface Classification feature, which streamlines an otherwise-tedious technical task while also giving sales teams a real competitive advantage. In this post we look at what it can do, how we’ve implemented it, and how to get started.
Can BGP routing tables provide actionable insights for both engineering and sales? Kentik Detect correlates BGP with flow records like NetFlow to deliver advanced analytics that unlock valuable knowledge hiding in your routes. In this post, we look at our Peering Analytics feature, which lets you see whether your traffic is taking the most cost-effective and performant routes to get where it’s going, including who you should be peering with to reduce transit costs.
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.
While Kentik Detect’s ground-breaking DDoS detection is field-proven to catch 30% more attacks than legacy systems, our DDoS capabilities aren’t limited to standalone detection. We’re also actively working with leading mitigation providers to create end-to-end DDoS protection solutions. So we’re excited to be partnering with A10 Networks, whose products help defend some of the largest networks in the world, to enable seamless integration of Kentik Detect with A10 Thunder TPS mitigation.
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.
Earlier this year the folks over at RouterFreak did a very thorough review of Kentik Detect. We really respected their thoroughness and the fact that they are practicing network engineers, so as we’ve come up with cool new gizmos in our product, we’ve asked them to extend their review. This post highlights some excerpts from their latest review, with particular focus on Kentik NPM, our enhanced network performance monitoring solution.
“NetFlow” may be the most common short-hand term for network flow data, but that doesn’t mean it’s the only important flow protocol. In fact there are three primary flavors of flow data — NetFlow, sFlow, and IPFIX — as well as a variety of brand-specific names used by various networking vendors. To help clear up any confusion, this post looks at the main flow-data protocols supported by Kentik Detect.
Destination-based Remotely Triggered Black-Hole routing (RTBH) is an incredibly effective and very cost-effective method of protecting your network during a DDoS attack. And with Kentik Detect’s new advanced Alerting system, automated RTBH is also relatively simple to configure. In this post, Kentik Customer Success Engineer Dan Rohan guides us through the process step by step.
Network performance is mission-critical for digital business, but traditional NPM tools provide only a limited, siloed view of how performance impacts application quality and user experience. Solutions Engineer Eric Graham explains how Kentik NPM uses lightweight distributed host agents to integrate performance metrics into Kentik Detect, enabling real-time performance monitoring and response without expensive centralized appliances.
In our second post related to BrightTalk videos recorded with Kentik at Cisco Live 2016, Kentik CEO Avi Freedman talks about the increasing threats that digital businesses face from DDoS and other forms of attacks and service interruptions. Avi also discusses the attributes that are required or desirable in a network visibility solution in order to effectively protect a network.
Cisco Live 2016 gave us a chance to meet with BrightTalk for some video-recorded discussions on hot topics in network operations. This post focuses on the first of those videos, in which Kentik’s Jim Frey, VP Strategic Alliances, talks about the complexity of today’s networks and how Big Data NetFlow analysis helps operators achieve timely insight into their traffic.
It was a blast taking part in our first ever Networking Field Day (NFD12), presenting our advanced and powerful network traffic analysis solution. Being at NFD12 gave us the opportunity to get valuable response and feedback from a set of knowledgeable network nerd and blogger delegates. See what they had to say about Kentik Detect…
BGP used to be primarily of interest only to ISPs and hosting providers, but it’s become something with which all network engineers should get familiar. In this conclusion to our four-part BGP tutorial series, we fill in a few more pieces of the puzzle, including when — and when not — it makes sense to advertise your routes to a service provider using BGP.
In this post we continue our look at BGP — the protocol used to route traffic across the interconnected Autonomous Systems (AS) that make up the Internet — by clarifying the difference between eBGP and iBGP and then starting to dig into the basics of actual BGP configuration. We’ll see how to establish peering connections with neighbors and to return a list of current sessions with useful information about each.
BGP is the protocol used to route traffic across the interconnected Autonomous Systems (AS) that make up the Internet, making effective BGP configuration an important part of controlling your network’s destiny. In this post we build on the basics covered in Part 1, covering additional concepts, looking at when the use of BGP is called for, and digging deeper into how BGP can help — or, if misconfigured, hinder — the efficient delivery of traffic to its destination.
In most digital businesses, network traffic data is everywhere, but legacy limitations on collection, storage, and analysis mean that the value of that data goes largely untapped. Kentik solves that problem with post-Hadoop Big Data analytics, giving network and operations teams the insights they need to boost performance and implement innovation. In this post we look at how the right tools for digging enable organizations to uncover the value that’s lying just beneath the surface.
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.
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.
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.
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.
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.
NetFlow and its variants like IPFIX and sFlow seem similar overall, but beneath the surface there are significant differences in the way the protocols are structured, how they operate, and the types of information they can provide. In this series we’ll look at the advantages and disadvantages of each, and see what clues we can uncover about where the future of flow protocols might lead.
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.
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.
Clear, comprehensive, and timely information is essential for effective network operations. For Internet-related traffic, there’s no better source of that information than NetFlow and BGP. In this series we’ll look at how we got from the first iterations of NetFlow and BGP to the fully realized network visibility systems that can be built around these protocols today.
Border Gateway Protocol (BGP) is a policy-based routing protocol that has long been an established part of the Internet infrastructure. Understanding BGP helps explain Internet interconnectivity and is key to controlling your own destiny on the Internet. With this post we kick off an occasional series explaining who can benefit from using BGP, how it’s used, and the ins and outs of BGP configuration.
By mapping customer traffic merged with topology and BGP data, Kentik Detect now provides a way to visualize traffic flow across across your network, through the Internet, and to a destination. This new Peering Analytics feature will primarily be used to determine who to peer (interconnect) with. But as you’ll see, Peering Analytics has use cases far beyond peering.
By actively exploring network traffic with Kentik Detect you can reveal attacks and exploits that you haven’t already anticipated in your alerts. In previous posts we showed a range of techniques that help determine whether anomalous traffic indicates that a DDoS attack is underway. This time we dig deeper, gathering the actionable intelligence required to mitigate an attack without disrupting legitimate traffic.
Kentik Detect is powered by Kentik Data Engine (KDE), a massively-scalable distributed HA database. One of the challenges of optimizing a multitenant datastore like KDE is to ensure fairness, meaning that queries by one customer don’t impact performance for other customers. In this post we look at the algorithms used in KDE to keep everyone happy and allocate a fair share of resources to every customer’s queries.
DDoS attacks impact profits by interrupting revenue and undermining customer satisfaction. To minimize damage, operators must be able to rapidly determine if a traffic spike is a true attack and then to quickly gather the key information required for mitigation. Kentik Detect’s Data Explorer is ideal for precisely that sort of drill-down.
Kentik Detect handles tens of billions of network flow records and many millions of sub-queries every day using a horizontally scaled distributed system with a custom microservice architecture. Instrumentation and metrics play a key role in performance optimization.
Kentik Detect’s alerting system generates notifications when network traffic meets user-defined conditions. Notifications containing details about the triggering conditions and the current status may be posted as JSON to Syslog and/or URL. This post shows how to parse the JSON with PHP to enable integration with external ticketing and configuration management systems.
Relational databases like PostgreSQL have long been dominant for data storage and access, but sometimes you need access from your application to data that’s either in a different database format, in a non-relational database, or not in a database at all. As shown in this “how-to” post, you can do that with PostgreSQL’s Foreign Data Wrapper feature.