IoT, 5G and the need for smart streaming

IoT, 5G and the need for smart streaming

September 06, 2019

IoT, 5 G and The Need for Smart Streaming

The Internet of Things (IoT) is about industry-specific applications, ecosystems and product designs. It’s about hardware, software and deployment methods. Take that to mean vertical industry-specific applications, product development, product design, deployment and more.

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This is a must-read for anyone interested in the world of Internet of Things (IoT). You’ll learn the IoT requirements in a 5G world, how 5G for enterprises will require other technologies to reap full benefit, how to implement data storage architectures poised for disruption, why the Western Digital data storage architectures are game-changing, and some other tips you may not have known about.

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The number of connected devices continues to rise, and with it, the amount of data generated will be astronomical. By 2020, Gartner estimates 20 billion internet-connected things — and with more connected devices, we’ll also see a massive surge in data generation.

It’s time to embrace 5G! The much anticipated emergence of 5G is happening now and will play a critical role in accelerating the realization of an increasingly connected world. 5G will change the way data is used from collection and analysis to prescriptive and predictive purposes to real-time decision making.

A major industry that will be affected by the data value moving to real-time is industries enabled by 5G and traditional communications service providers. Companies are already beginning to think about their systems in terms of an event, decision, notification, and action loop.

To be successful with real-time data architectures you need to scale to ingest large amounts of data traffic. The architectures themselves must be dynamic, allowing them to make intelligent decisions across multiple data streams.

 

IoT, 5G and the need for smart streaming

By combining state management and stream data processing, we will enable the combination of IoT, 5G, and traditional databases and streaming technologies.

The key to low latency 5G is to have low latency processing. To achieve this, a unified architecture that brings together the state management of a database and the ability to handle streams of data contextually becomes necessary.

As decision-making requirements are moving closer to the edge, a distributed architecture of data processing that can reside in low footprint edge data centers is key. Disparate technologies operating in unison to ingest, process and store data, not only results in higher latency, but also leads to complications with scale, handling complex workloads and hardware sprawl.

Streaming data processing has gone beyond just processing and reprocessing to active decision making. To move from this traditional approach to more advanced streaming analytics, we must think outside the box and look at the different ways in which we can perform analysis.

Your organization needs to adapt its traditional architecture to exploit the potential of 5G-powered applications.

Keys to A Successful Stateful streaming Implementation

Key ingredients for a successful stateful streaming implementation. You should organize your data according to state machine and ledgering. State machine is where the last known state of the entities in a system is maintained.

Real-time decisions on streaming data require both the state machine and a contextual ledger to play together while the forever data is used to retrain the machine learning layers in the data architecture.

Each stateful stream machine has to integrate bi-directionally with the learning machines. The learning machine will receive new training data from the stateful streaming machine, while the stateful streaming machine will receive updated insight, rules and business logic from the learning machine.

This is the right solution for a lot of different situations, especially where your business transactions involve data that’s changing so quickly it’s hard to reconcile. In a lot of cases, the data doesn’t change very often, but the time between when it changes is critical, so you need to process it as it happens.

As organizations become able to process large amounts of real-time data, they’ll be able to deliver value quicker and more intelligently, resulting in better monetization of real-time data.

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