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Kentik Introduces Post-Hadoop Network Traffic Analytics

Kentik announced the release of significant enhancements to Kentik Detect, its big data network analytics solution.

New features include multi-dimensional traffic analytics, enabling access to billions of possible analyses operating on trillions of instantly accessible data records, new traffic flow visualizations, and support for network performance metrics.

"Kentik is the first network traffic analytics solution built from the ground up on a big data platform, offered both as a multi-tenant SaaS as well as an on-premises deployment," said Avi Freedman, Kentik CEO. "Our latest enhancements illustrate the power of the Kentik platform to deliver analytics capabilities previously unavailable to anyone but webscale giants. In 15 minutes, customers can get from registration to production use of big data analysis that goes beyond Map/Reduce and streaming analytics-based approaches, with no appliances or software deployment required."

The Kentik portal updates, available immediately, offer multi-dimensional analytics, which provides users with instant access to any one of billions of possible network traffic and performance analyses. Users can select, group, order, filter and visualize several data dimensions from dozens of data fields in mere seconds, even when operating on trillions of data records.

Enhanced visualizations include time-series line, bar and stacked line charts, comparison bar charts, and traffic flow charts. Users can analyze traffic based on expanded metrics that address traffic volume, such as bits, packets and flows per second; endpoints, such as unique source and destination IP addresses; and network performance, such as TCP retransmits, jitter, and round-trip time.

Kentik's instant multi-dimensional analytics contrasts with traditional big data analytics platforms such as those based on Hadoop's Map/Reduce. Hadoop, the best known big data technology, requires a lengthy process to prepare data for analysis, and full scans of data to provide analytics. These approaches can take hours to complete, making it too slow for operational use cases. Alternate approaches involve creating pre-defined aggregates or "data cubes" comprised of multiple data dimensions, with fixed dimensions and limited granularity.

Kentik ingests network data at Internet scale--hundreds of billions of records per day--in real-time, and allows users to run multi-dimensional queries and receive visualizations in a few seconds. This allows Kentik to support key network visibility use cases at greater scale and detail than previously possible.

Kentik makes network operations more intelligent, providing intuitive visualizations for network planning and BGP peering analytics, thus improving user experience and network cost efficiency. Kentik's analytical muscle makes it the ideal DDoS detection platform since it can scan massive volumes of data on a network-wide basis and alert on both attacks and operational anomalies in seconds rather than the multi-minute timeframes of traditional traffic analysis solutions.

"Network data is big data," said Jim Frey, Kentik VP Product. "Without scale, granularity, flexibility and speed of analysis, it's impossible to get actionable intelligence from it. Kentik unlocks the value of network data for network managers and operators, so they can improve service delivery and focus on improvements and innovation rather than reactive firefighting."

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Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

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Kentik Introduces Post-Hadoop Network Traffic Analytics

Kentik announced the release of significant enhancements to Kentik Detect, its big data network analytics solution.

New features include multi-dimensional traffic analytics, enabling access to billions of possible analyses operating on trillions of instantly accessible data records, new traffic flow visualizations, and support for network performance metrics.

"Kentik is the first network traffic analytics solution built from the ground up on a big data platform, offered both as a multi-tenant SaaS as well as an on-premises deployment," said Avi Freedman, Kentik CEO. "Our latest enhancements illustrate the power of the Kentik platform to deliver analytics capabilities previously unavailable to anyone but webscale giants. In 15 minutes, customers can get from registration to production use of big data analysis that goes beyond Map/Reduce and streaming analytics-based approaches, with no appliances or software deployment required."

The Kentik portal updates, available immediately, offer multi-dimensional analytics, which provides users with instant access to any one of billions of possible network traffic and performance analyses. Users can select, group, order, filter and visualize several data dimensions from dozens of data fields in mere seconds, even when operating on trillions of data records.

Enhanced visualizations include time-series line, bar and stacked line charts, comparison bar charts, and traffic flow charts. Users can analyze traffic based on expanded metrics that address traffic volume, such as bits, packets and flows per second; endpoints, such as unique source and destination IP addresses; and network performance, such as TCP retransmits, jitter, and round-trip time.

Kentik's instant multi-dimensional analytics contrasts with traditional big data analytics platforms such as those based on Hadoop's Map/Reduce. Hadoop, the best known big data technology, requires a lengthy process to prepare data for analysis, and full scans of data to provide analytics. These approaches can take hours to complete, making it too slow for operational use cases. Alternate approaches involve creating pre-defined aggregates or "data cubes" comprised of multiple data dimensions, with fixed dimensions and limited granularity.

Kentik ingests network data at Internet scale--hundreds of billions of records per day--in real-time, and allows users to run multi-dimensional queries and receive visualizations in a few seconds. This allows Kentik to support key network visibility use cases at greater scale and detail than previously possible.

Kentik makes network operations more intelligent, providing intuitive visualizations for network planning and BGP peering analytics, thus improving user experience and network cost efficiency. Kentik's analytical muscle makes it the ideal DDoS detection platform since it can scan massive volumes of data on a network-wide basis and alert on both attacks and operational anomalies in seconds rather than the multi-minute timeframes of traditional traffic analysis solutions.

"Network data is big data," said Jim Frey, Kentik VP Product. "Without scale, granularity, flexibility and speed of analysis, it's impossible to get actionable intelligence from it. Kentik unlocks the value of network data for network managers and operators, so they can improve service delivery and focus on improvements and innovation rather than reactive firefighting."

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In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.