Edge to Cloud—Defining a Framework for Edge Analytics

Introduction

The adoption of edge computing is quickly catching on, and companies are looking for ways to maximize their investments in data centers. Edge analytics can help by providing a framework for integrating cloud platforms with edge devices, such as IoT sensors and mobile devices. This framework consists of four key areas: data acquisition, storage, processing, and delivery. Combined together, these elements make up an end-to-end solution that delivers actionable insights in real time based on data collected at the edge.

The growth of edge computing

With the number of IoT devices growing at an exponential rate, it’s no surprise that edge computing is on the rise. According to IDC, global spending on IoT will be $8 trillion by 2022–a staggering figure that highlights how important it is for companies to keep up with emerging technologies like edge analytics.

Here are some examples of companies who have embraced this trend:

• [IBM](https://www.*ibm.*com/) has invested heavily in its cloud platform and services offerings over the past few years as well as developing new products specifically designed for edge computing environments. The company hopes these initiatives will give them an advantage over their competitors when it comes time for customers to choose where they want their data processed (i.e., “in” or “at”) when working with IBM’s cloud offerings.*
• [Amazon Web Services (AWS)](https://awscloud.*amazonwebservices.)com/) offers several different toolsets designed specifically for use cases involving remote locations such as mines or oil rigs; however, these tools aren’t limited only

Cloud platforms and edge analytics integration

As a result, many cloud platforms are not designed for edge analytics. They were built to support cloud-based applications and infrastructure that require thousands of nodes spread over multiple data centers. These large-scale deployments serve as an ideal test bed for developing next generation software architectures that can handle billions of transactions per second (tps).

However, the requirements of an edge device are different than those in a traditional cloud environment. First off, they’re typically much smaller–typically just one or two devices instead of hundreds or thousands. Second, they’re often deployed in remote locations where there isn’t access to reliable power sources or sufficient bandwidth–so they need more compute power and memory/storage capacity than most modern servers do today. Lastly: because these devices run on batteries instead of AC power grids (and thus must conserve energy), any additional processing done locally will reduce battery life significantly if not carefully managed by developers who understand how much work each task takes on average versus what type(s) might benefit from being moved closer toward end users’ devices rather than being done remotely from centralized servers back at headquarters.”

Defining a framework for an edge-to-cloud analytics solution

To get started, you need to define the problem. What is it that you want to accomplish?

The first step in defining a framework for an edge-to-cloud analytics solution is thinking about what goals you have and how they might be achieved. Before we dive into specific technical details, let’s start by defining some high-level goals:

• Define your overall fitness goal. This could be anything from getting stronger or losing weight, to improving your posture or increasing flexibility. It should be something that motivates and inspires you–something practical but also aspirational enough so that when things get hard (and they will), you won’t give up on yourself! Make sure this goal aligns with other aspects of your life; if getting fit helps improve relationships with friends and family members who support healthy habits then great! But if working out makes them feel uncomfortable because they don’t like being around sweaty people then maybe this isn’t going work out so well… just saying!
• Set smaller milestones along the way towards reaching larger ones. For example: If one month into working out regularly I notice myself feeling stronger than before then perhaps my next milestone could be doing pushups without resting between sets (instead of just 5 repetitions).

Learn how to define a framework for an edge-to-cloud analytics solution.

The Edge to Cloud Framework is a simplified approach to defining an analytics solution that leverages edge computing, cloud storage, and advanced analytics. It consists of four components:

• The data lake on the edge
• The gateway connecting the edge and cloud
• An analytics layer running on top of the gateway
• An application layer running in your environment (on premises or in a public cloud)

Conclusion

The growth of edge computing and its role in the future of analytics is undeniable. It’s a natural progression from on-premises solutions to cloud platforms that can be accessed anywhere, anytime. As data becomes more ubiquitous, we will need new ways to analyze it quickly and efficiently before making decisions based on those insights. This means developing solutions that can handle large amounts of information at once while still being able to run on devices with limited resources such as smartphones or IoT devices–which makes edge computing an ideal solution for these scenarios!