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Let There Be Light - Creating Order Out of Chaos in Big Data

"Big Data" is among the hottest topics in business today. Executives want to know how to gain actionable insights and make decisions from the flood of data and metadata pouring out of their networks. That's good – it's their job to look for any way they can increase sales, reduce waste, and generally improve their business efficiency. But to get to those actionable insights, you first have to make some kind of sense of all this data.

Volume, Velocity and Variety

The three attributes of Big Data are volume, velocity and variety. Each one brings its own challenge to your network infrastructure and specifically to the network monitoring system you use to collect, capture and analyze your data.

Big Data flows out of Big Networks – the high-capacity architecture that supports a previously inconceivable amount of commerce and communication. You need to tap into that gigantic flow of data, recognize what you're seeing, and organize it for the deep analysis that yields the answers you're looking for.

To do all that, you need intelligent network monitoring switches that are big enough and fast enough to work at the volume and velocity of the data you're after. They also need to be able to identify and organize the variety of data flowing through your network. The network monitoring switch must possess the capability to create order out of chaos of this massive data flow.

How Much Data Can You Afford To Analyze?

In the business world, nothing of value comes for free. The tools required to analyze your data and get the answers you need are not cheap. Big Data can easily overwhelm individual tools – and you can't get the true answer by sampling a little bit of Big Data here and there. You need to own all the data to get the whole picture, and that can run up a huge expense.

An innovative network data collection strategy, based on intelligent network monitoring switches, will let you tame the torrent. You can render Big Data manageable with a much smaller set of tools, and that keeps your network analysis costs under control.

Intelligent Network Monitoring

Today's intelligent network monitoring switches can gather, collate, filter, process and distribute packets to analysis tools, assuring data visibility, stability, security and optimization of your tool investment.

Here are a few features of state-of-the-art intelligent network monitoring switches that make it possible to manage Big Data:

- Packet deduplication culls the stream of duplicate information that can make up 40% of network monitoring system traffic. You need to eliminate duplication to get a good look at the real data. Filtering out duplicate packets also saves money because you're not buying multiple tools or incremental tool licenses to analyze the same data over and over again.

- Packet slicing strips data packets of bits that are unnecessary for certain tools. Packet payloads can be removed for IDS tools that do not need payload information to perform their work. Credit card numbers and social security numbers can be sliced away when packets are sent to traffic analysis tools. This lightens the load while serving the dual purpose of increasing throughput efficiency and maintaining security regulatory compliance.

- Time stamping allows you to know the exact moment – within fewer than 10 nanoseconds – when some event happened on your network, in precise relation to the last event and the next event. With Big Data, when something happened can be as important as what happened. By stamping each packet with its exact time of entry, you create a new level of metadata that allows your analysis tools to precisely reconstruct a sequence of events.

- Multi Stage Filtering techniques simplify the process of sorting unstructured data. To be used effectively, each analysis tool needs to receive a complete set of accurate traffic; nothing more and definitely nothing less. Multi Stage Filtering takes a Big Data input stream and directs it through a series of filters that you design, carefully sorting the individual data packets and directing them to tools or to additional filters for pinpoint accuracy. When you eliminate irrelevant packets from a tool's input stream, you get the full value of your data without wasting resources.

There's more, but these are the newest features that allow intelligent network monitoring to reduce and organize Big Data into something you can use to understand the flow of activity in your business more effectively. Intelligent network monitoring turns on the light to let you see Big Data clearly.

ABOUT Richard Rauch

Richard Rauch, President and CEO of APCON, founded the company in 1993 to provide state-of-the-art network connectivity to a wide variety of industries. Today, he is the driving force behind the research and development of APCON networking technology, and has built the company into a leading supplier of intelligent network monitoring products.

Related Links:

www.apcon.com

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Let There Be Light - Creating Order Out of Chaos in Big Data

"Big Data" is among the hottest topics in business today. Executives want to know how to gain actionable insights and make decisions from the flood of data and metadata pouring out of their networks. That's good – it's their job to look for any way they can increase sales, reduce waste, and generally improve their business efficiency. But to get to those actionable insights, you first have to make some kind of sense of all this data.

Volume, Velocity and Variety

The three attributes of Big Data are volume, velocity and variety. Each one brings its own challenge to your network infrastructure and specifically to the network monitoring system you use to collect, capture and analyze your data.

Big Data flows out of Big Networks – the high-capacity architecture that supports a previously inconceivable amount of commerce and communication. You need to tap into that gigantic flow of data, recognize what you're seeing, and organize it for the deep analysis that yields the answers you're looking for.

To do all that, you need intelligent network monitoring switches that are big enough and fast enough to work at the volume and velocity of the data you're after. They also need to be able to identify and organize the variety of data flowing through your network. The network monitoring switch must possess the capability to create order out of chaos of this massive data flow.

How Much Data Can You Afford To Analyze?

In the business world, nothing of value comes for free. The tools required to analyze your data and get the answers you need are not cheap. Big Data can easily overwhelm individual tools – and you can't get the true answer by sampling a little bit of Big Data here and there. You need to own all the data to get the whole picture, and that can run up a huge expense.

An innovative network data collection strategy, based on intelligent network monitoring switches, will let you tame the torrent. You can render Big Data manageable with a much smaller set of tools, and that keeps your network analysis costs under control.

Intelligent Network Monitoring

Today's intelligent network monitoring switches can gather, collate, filter, process and distribute packets to analysis tools, assuring data visibility, stability, security and optimization of your tool investment.

Here are a few features of state-of-the-art intelligent network monitoring switches that make it possible to manage Big Data:

- Packet deduplication culls the stream of duplicate information that can make up 40% of network monitoring system traffic. You need to eliminate duplication to get a good look at the real data. Filtering out duplicate packets also saves money because you're not buying multiple tools or incremental tool licenses to analyze the same data over and over again.

- Packet slicing strips data packets of bits that are unnecessary for certain tools. Packet payloads can be removed for IDS tools that do not need payload information to perform their work. Credit card numbers and social security numbers can be sliced away when packets are sent to traffic analysis tools. This lightens the load while serving the dual purpose of increasing throughput efficiency and maintaining security regulatory compliance.

- Time stamping allows you to know the exact moment – within fewer than 10 nanoseconds – when some event happened on your network, in precise relation to the last event and the next event. With Big Data, when something happened can be as important as what happened. By stamping each packet with its exact time of entry, you create a new level of metadata that allows your analysis tools to precisely reconstruct a sequence of events.

- Multi Stage Filtering techniques simplify the process of sorting unstructured data. To be used effectively, each analysis tool needs to receive a complete set of accurate traffic; nothing more and definitely nothing less. Multi Stage Filtering takes a Big Data input stream and directs it through a series of filters that you design, carefully sorting the individual data packets and directing them to tools or to additional filters for pinpoint accuracy. When you eliminate irrelevant packets from a tool's input stream, you get the full value of your data without wasting resources.

There's more, but these are the newest features that allow intelligent network monitoring to reduce and organize Big Data into something you can use to understand the flow of activity in your business more effectively. Intelligent network monitoring turns on the light to let you see Big Data clearly.

ABOUT Richard Rauch

Richard Rauch, President and CEO of APCON, founded the company in 1993 to provide state-of-the-art network connectivity to a wide variety of industries. Today, he is the driving force behind the research and development of APCON networking technology, and has built the company into a leading supplier of intelligent network monitoring products.

Related Links:

www.apcon.com

Hot Topics

The Latest

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.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...

Seamless shopping is a basic demand of today's boundaryless consumer — one with little patience for friction, limited tolerance for disconnected experiences and minimal hesitation in switching brands. Customers expect intuitive, highly personalized experiences and the ability to move effortlessly across physical and digital channels within the same journey. Failure to deliver can cost dearly ...

If your best engineers spend their days sorting tickets and resetting access, you are wasting talent. New global data shows that employees in the IT sector rank among the least motivated across industries. They're under a lot of pressure from many angles. Pressure to upskill and uncertainty around what agentic AI means for job security is creating anxiety. Meanwhile, these roles often function like an on-call job and require many repetitive tasks ...