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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

AI is becoming the operating system of the enterprise. It acts as an invisible coordination layer that understands intent, connects systems, and executes work across complex SaaS environments. Previously, employees had to click through multiple systems — CRM, ERP, support tools, collaboration platforms — to complete a single task. Now, instead of navigating each application manually, they can simply state what they need to accomplish ...

In 2026, the cost of downtime or an outage is no longer just a technical inconvenience; it's a $600 billion wake up call for global businesses. As our digital ecosystems become  more interconnected, each touchpoint introduces new risks and multiplies the consequences when things go wrong. And the data is clear: aggregate downtime costs  for Global 2,000 companies have surged 50% since 2024, reaching a staggering $600 billion ...

Deloitte found that 74% of enterprises expect to deploy agentic AI solutions in the next 24 months. However, the rush to deployment is outpacing foundational work, though. Only 21% of enterprises have fully formed agent governance models in place. The result? AI agents deployed without guidance or governance begin to function as fragmented islands of complexity ...

Cloud spending is no longer viewed as a passthrough IT expense, but as a strategic financial lever that directly impacts innovation capacity, profitability and enterprise resilience, according to the CFO Cloud Cost Optimization Report from Azul ...

As AI moves from generating responses to performing actions, the need for trust increases exponentially. And as organizations enlist AI agents for increasingly sophisticated business processes, trust is going to be the single most important theme for spurring adoption. What can organizations do to build trustworthy AI agents? ...

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...