Understanding the Network Monitoring Needs of Real-Time Data Streaming

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More advanced data means more advanced network monitoring.

Big data is beginning to shape our daily lives in ways we could never have imagined a few years ago. Recent studies suggest we’re on track to see a data landscape of 44 zettabytes, or 44 trillion gigabytes, by 2020.

Compare this to the 4.4 zettabytes we currently have and it’s easy to see the explosive growth that’s about to occur. But current technological limitations only let us analyze and utilize approximately 0.5 percent of this data — the rest goes to waste.

Statistics like the ones above highlight the need for stringent networking monitoring protocols. Not only will these best practices help your organization rein in all of this data, but it also ensures you’re operating in compliance with any current or future laws, standards or trends.

Monitoring Data in Real-Time

Organizations need to establish combined teams of IT experts and application management professionals to accommodate these massive datasets. The sheer volume of data and the various sources require a multidisciplinary staff that is well-equipped to make sense of the incoming information.

But they also need the right hardware to support the incoming information. The team with Shimane Fujitsu recently used cloud computing and IoT-connected sensors to create a comprehensive visualization model of their product rework station, ultimately resulting in greater access to information regarding deadlines and shipping dates as well as reduced shipping costs and lower lead times.

Acting On This Data

Collecting and monitoring incoming data is just the beginning. Your organization needs to act quickly if you hope to make the biggest impact with big data analytics:

1. Real-Time Processing

Information that utilizes a constant input and output of data that requires instant streaming, processing, management and utilization. Generic examples include bank ATMs, weather radar systems and retail point-of-sale devices.

Centrak uses a real-time location system, or RTLS, to track daily workflow and maximize customer returns. Their technology delivers actionable data directly to doctors, nurses and executive-level officials. While Centrak’s services are currently used exclusively in the healthcare sector, this kind of technology is easily transferable to other industries.

Real-time data processing is also useful in big data management, archival and warehousing. Although it was first used by stock exchanges during the transition to electronic trading, streaming data collection and processing are useful in nearly every industry today.

Machine-based algorithms are often seen in real-time fraud detection. By analyzing and highlighting various habits, trends, and patterns, today’s real-time processing systems can identify which activities have the potential for fraud and mark them for further investigation. In cases like this, both the machine and the human worker are necessary to complete the process.

2. Near-Real-Time Processing

This is a common alternative that can reduce computational needs and overhead costs when compared to real-time processing. This method is still fast, but typically delivers results in minutes instead of seconds.

A microwave vehicle detection system, or MVDS, was recently deployed near the city of Orlando to monitor vehicle traffic and reduce congestion. By scheduling their monitors to archive traffic flow data on a per-minute basis, researchers utilized a near-real-time processing system. They were ultimately able to identify periods of higher congestion and issue the appropriate warnings to drivers ahead of time.

Batch processing is used when time isn’t as important. This method could take hours, days or even weeks. Although it’s not as fast, this process often results in deeper analytics and greater business intelligence.

A leading IT solutions provider in India recently implemented near-real-time processing into their backend transaction processing system. The upgrade resulted in a number of measurable benefits, including:

  • Vast reductions in both the time and cost of processing. They reduced their per-project time by a factor of greater than 10.
  • Improved optimization of network resources. They achieved a total of 65% cluster utilization throughout the entire system.

The company improved processing times on 100,000 items from 12 hours to 50 minutes and cut processing costs by almost 70%. Parallel processing was also implemented, cutting project times even further. With their current system, the company can handle 800 records per minute at cost of 10 cents per 1,000 items processed.

Continuing Productivity During Emergencies

Modern companies also need to worry about business continuity and disaster planning. Whether it’s something as simple as an hour-long power outage or something much worse, it’s critical to have a contingency plan in place.

The University of Wisconsin-Madison recently suffered a major power outage. Email, voicemail and even websites were suddenly left in the dark. Some systems reverted to offline backups, like the library’s book checkout system, but others were inaccessible for several hours. In this case, the school resorted to social media and SMS to deliver important messages to students and faculty members.

Realizing the Importance of Big Data and Network Monitoring

Businesses and organizations that fail to introduce best practices around big data streaming and analytics face numerous challenges.

In the above case studies alone, the presence of established protocols results in greater production workflow, increased access to information and improved public safety.

With specific examples like this across every industry and every profession, it’s easy to see how important real-time network monitoring has become.

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