Understanding Fast Data and its Importance in an IoT-driven world

Internet of Things and now Industrial Internet of Things , both are making great impact in the World so lots of people are analyzing the impact the Internet of Things and the Industrial Internet of Things on the near future. How the things are changing by these technologies , how are they effecting daily life and lots of research have been continuing.

It’s undoubtedly important to do that, but before thinking the IoT and IIoT in detail, it’s necessary to understand the role “fast data” in general will take in helping these technologies work.

First, let’s define fast data. Big data is often created by data that is generated at incredible speeds, such as click-stream data, financial ticker data, log aggregation, or sensor data. Often these events occur thousands to tens of thousands of times per second. No wonder this type of data is commonly referred to as a “fire hose.”

When we talk about fire hoses in big data, we’re not measuring volume in the typical gigabytes, terabytes, and petabytes familiar to data warehouses. We’re measuring volume in terms of time: the number of megabytes per second, gigabytes per hour, or terabytes per day. We’re talking about velocity as well as volume, which gets at the core of the difference between big data and the data warehouse. Big data isn’t just big; it’s also fast.

So we can say, Fast data processes high volumes and continuous streams of data in real-time with low to medium latency. Together, the speed and lack of delays allow businesses to make in-the-moment decisions based on insights gleaned from the data.

Besides the efficiency associated with fast data, people are interested in it because of the potential it offers to enterprises of all types and sizes. It’s scalable, has a high uptime and can quickly recover from failure situations.

Those characteristics make corporate leaders realize why fast data is worth researching to potentially apply to their business models.

People utilizing fast data in the IIoT must have specialized equipment. There are two main components of a fast data setup, and business leaders can begin researching the options to get prepared as new capabilities emerge.

Capturing value in fast data

The best way to capture the value of incoming data is to react to it the instant it arrives. If you are processing incoming data in batches, you’ve already lost time and, thus, the value of that data.

To process data arriving at tens of thousands to millions of events per second, you will need two technologies: First, a streaming system capable of delivering events as fast as they come in; and second, a data store capable of processing each item as fast as it arrives.

Delivering the fast data 

Two popular streaming systems have emerged over the past few years: Apache Storm and Apache Kafka. Originally developed by the engineering team at Twitter, Storm can reliably process unbounded streams of data at rates of millions of messages per second. Kafka, developed by the engineering team at LinkedIn, is a high-throughput distributed message queue system. Both streaming systems address the need of processing fast data. Kafka, however, stands apart.

Kafka was designed to be a message queue and to solve the perceived problems of existing technologies. It’s sort of an über-queue with unlimited scalability, distributed deployments, multi tenancy, and strong persistence. An organization could deploy one Kafka cluster to satisfy all of its message queuing needs. Still, at its core, Kafka delivers messages. It doesn’t support processing or querying of any kind.

Investigating what’s available now gives companies a leg up to prepare for the increasing prominence of IoT technologies. Being proactive also gives business leaders a chance to think about how they can use fast data most effectively to get closer to their goals.

There are several ways fast data aligns with business objectives.

As the IoT becomes more prominent than ever, the gadgets people use every day increasingly have Wi-Fi-enabled sensors that collect data and give personalized information.

Businesses might also purchase store signs that use personalization to predict and cater to customer needs based on past purchases. They could also install security cameras that use prescriptive analytics to reduce incidents of theft and catch things human loss prevention experts may miss.

Collectively, these things lead to more profits and smarter ways of doing business.

Companies can maintain more competitiveness in the marketplace, provided they have a solid understanding of data analytics and fast data.