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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

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...