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Hydrolix and Mux Partner to Deliver Real-Time Observability from Origin to Edge for Live Event, Video Streaming

Poor video performance, outages and pirated streams can cause significant issues for streaming — and these issues can be catastrophic for live events where demand is high and viewers don't want to miss a single second. To help streaming companies immediately see and respond to these issues, Hydrolix, the streaming data lake company disrupting the economics of big data, has teamed with Mux, the company behind the leading video streaming and monitoring platform for developers, to extend infrastructure observability to every screen.

Now, through the partnership with Hydrolix, publishers can use Mux Data along with all the other observability data they need to maximize infrastructure performance, from content creation to delivery...every bit of data can be kept and queried for rapid response and deep insights.

The Hydrolix platform ingests, stores and queries terabytes of log data while providing cost-effective, long-term storage, and sub-second query latency on all of the data — whether it's a minute or a year old. This makes Hydrolix an ideal fit for origin-to-edge observability and content delivery network (CDN) monitoring, which typically generate large volumes of data.

Mux Data provides comprehensive metrics for a deep understanding of video quality of experience across applications available on all device platforms. With Mux, streaming companies can quickly detect and fix potential quality of experience issues like playback failures, long video startup time, rebuffering latency and video quality.

Combined, Hydrolix retains and analyzes data from Mux along with origin and edge services log data and presents critical insights in one dashboard so that on-demand streamers can immediately spot quality of experience issues throughout their content's digital journey.

The Mux integration is compatible with Cascade, Hydrolix's managed observability service for AWS; TrafficPeak, an Akamai Cloud observability solution powered by Hydrolix; and other solutions powered by the Hydrolix streaming data lake.

Combined, Hydrolix and Mux provide unique benefits that other platforms can't provide, including:

- Next-level CDN monitoring — now with video analytics included

- Real-time origin-to-edge observability including for major events generating millions of log lines per second

- Long-term historical analytics for a deep understanding of user engagement and trends over time

"Hydrolix has solved the problem of how to store and query all of your observability data in real time, at scale, and without breaking the bank," said Marty Kagan, co-founder and CEO of Hydrolix. "Mux has solved the problem of how to get comprehensive visibility into video players to see how users are engaging with their content. When you put these two incredible technologies together, you get something event and video streamers have never had before: affordable, real-time observability at scale from origin to edge in one dashboard."

"Mux is committed to helping publishers deliver content better by deeply understanding the viewer experience, and that's why our platform is the trusted infrastructure for the world's largest streaming events," said Jon Dahl, co-founder and CEO of Mux. "Now, through our partnership with Hydrolix, publishers can use Mux Data along with all the other observability data they need to maximize infrastructure performance, from content creation to delivery. Everything is in one place, and every bit of data can be kept and queried for rapid response and deep insights."

The Latest

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.

Hydrolix and Mux Partner to Deliver Real-Time Observability from Origin to Edge for Live Event, Video Streaming

Poor video performance, outages and pirated streams can cause significant issues for streaming — and these issues can be catastrophic for live events where demand is high and viewers don't want to miss a single second. To help streaming companies immediately see and respond to these issues, Hydrolix, the streaming data lake company disrupting the economics of big data, has teamed with Mux, the company behind the leading video streaming and monitoring platform for developers, to extend infrastructure observability to every screen.

Now, through the partnership with Hydrolix, publishers can use Mux Data along with all the other observability data they need to maximize infrastructure performance, from content creation to delivery...every bit of data can be kept and queried for rapid response and deep insights.

The Hydrolix platform ingests, stores and queries terabytes of log data while providing cost-effective, long-term storage, and sub-second query latency on all of the data — whether it's a minute or a year old. This makes Hydrolix an ideal fit for origin-to-edge observability and content delivery network (CDN) monitoring, which typically generate large volumes of data.

Mux Data provides comprehensive metrics for a deep understanding of video quality of experience across applications available on all device platforms. With Mux, streaming companies can quickly detect and fix potential quality of experience issues like playback failures, long video startup time, rebuffering latency and video quality.

Combined, Hydrolix retains and analyzes data from Mux along with origin and edge services log data and presents critical insights in one dashboard so that on-demand streamers can immediately spot quality of experience issues throughout their content's digital journey.

The Mux integration is compatible with Cascade, Hydrolix's managed observability service for AWS; TrafficPeak, an Akamai Cloud observability solution powered by Hydrolix; and other solutions powered by the Hydrolix streaming data lake.

Combined, Hydrolix and Mux provide unique benefits that other platforms can't provide, including:

- Next-level CDN monitoring — now with video analytics included

- Real-time origin-to-edge observability including for major events generating millions of log lines per second

- Long-term historical analytics for a deep understanding of user engagement and trends over time

"Hydrolix has solved the problem of how to store and query all of your observability data in real time, at scale, and without breaking the bank," said Marty Kagan, co-founder and CEO of Hydrolix. "Mux has solved the problem of how to get comprehensive visibility into video players to see how users are engaging with their content. When you put these two incredible technologies together, you get something event and video streamers have never had before: affordable, real-time observability at scale from origin to edge in one dashboard."

"Mux is committed to helping publishers deliver content better by deeply understanding the viewer experience, and that's why our platform is the trusted infrastructure for the world's largest streaming events," said Jon Dahl, co-founder and CEO of Mux. "Now, through our partnership with Hydrolix, publishers can use Mux Data along with all the other observability data they need to maximize infrastructure performance, from content creation to delivery. Everything is in one place, and every bit of data can be kept and queried for rapid response and deep insights."

The Latest

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.