This guest post brings a security perspective to bear on network visibility and analysis. Information security executive and analyst David Monahan underscores the importance of being able to collect and contextualize information in order to protect the network from malicious activity. Monahan explores the capabilities needed to support numerous network and security operations use cases, and describes Kentik Detect as a next-generation flow analytics solution with high performance, scalability, and flexibility.
Today’s MSPs frequently find themselves without the insights needed to answer customer questions about network performance and security. To maintain customer confidence, MSPs need answers that they can only get by pairing infrastructure visibility with traffic analysis. In this post, guest contributor Alex Hoff of Auvik Networks explains how a solution combining those capabilities enables MSPs to win based on customer service.
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.
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.
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.
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.
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.