You can categorize Edge computing as the practice of capturing, storing, processing, and analyzing data closer to the consumer, where the application data is created. With cloud platforms, this extension removes the cloud as the primary resource for all data processing and allows collection and processing at the Edge.

Some of the advantages that Edge computing provides are as follows:

• Data can be captured, analyzed, and processed near the client without relying on cloud provider integration.
• Compute power is now available on the edge thereby reducing compute costs in the cloud.
• Various edge type models such as Internet of Things (IoT), manufacturing, financial, automotive, and Augmented Reality/Virtual Reality are greatly enhanced with 5G to support wide-area coverage, low latency, and cost-efficiency.

As you know, 5G is now being rolled out and with that, these new business models are being added to the digital transformation. 5G will improve performance and many Telcos are preparing to move more processing power to the edge. Figure 16.6 provides a graphic on how Edge computing enables the capturing, storing, processing, and analyzing of data near the client, where the data is generated, instead of in a centralized data-processing warehouse:

Figure 16.6 – Edge Computing

In Figure 16.6, the circled areas represent APIs running on various implementations that could be phones, kiosks, and other consumers. By building out an edge messaging integration, businesses can have a re-imagined infrastructure where they can take advantage of the speed of 5G, represented by the cell towers, with extremely low latency.

IBM is preparing for this journey too. With the purchase of Turbonomic, it will use Turbonomic network performance management tools for enterprise 5G deployments.

While Edge computing is still in its infancy, you can imagine the types of capabilities that can be accomplished using 5G and the edge. Just think, events are triggered from data sources and received by devices immediately, whether it’s database updates, messages/events, or secure streaming. All this can be possible as you move towards edge computing with 5G.

Not only did you learn about how AI can assist with digital transformation automation but also about the next extension of cloud technology, Edge computing. With Edge computing, you learned why it provides value above and beyond just using a cloud platform. It allows data to be closer to the edge where it impacts the user the most. As Telcos continue to upgrade their networks with 5G, more business models will appear that support improved AR/VR, IoT, financial and automotive industries, and others.

Summary

In this chapter, we discussed the impact of the COVID-19 pandemic and how it rewarded those who have achieved some digital transformation and motivated others to accelerate their efforts. With all change there is opportunity. You learned that as you move towards a hybrid cloud implementation, some considerations must be made about the platform and toolset you utilize to ensure you maintain the flexibility necessary to stay agile and cost-aware. You were introduced to OpenShift as a platform to address learning curves of existing resources as well as to provide a common platform that can support multi-cloud, on-premise, and portability. You also learned that OpenShift is more adaptable and provides more out-of-the-box features than DIY Kubernetes. You learned that DIY requires your organization to do the following:

• Build a full DIY Kubernetes stack
• Test your Kubernetes stack
• Stay abreast of Kubernetes changes
• Patch security CVEs (weekly)
• Troubleshoot Kubernetes stack problems
• Repair Kubernetes problems

Utilizing OpenShift helps organizations move toward modernization more comfortably.

You also learned about IBM’s hybrid cloud enabling using Cloud Pak for Integration (CP4I). Integration is still key whether you are running on-premise or in one or many clouds. Digital transformation may be a mix of various technologies and SaaS applications and integrating that mix will take the proper set of tooling.

The combination of OpenShift and CP4I is proven technology that improves the success factors of digital transformation and modernization.

You also learned about how AI is taking IT operations to a new level of efficiency. With AIOps the vast amount of data collected in events and logs can be utilized to allow machine learning to improve operations. Challenges that hindered people-centric operations such as time to troubleshoot and the remediation of outages have been reduced and are more proactive than reactive.

Finally, you learned about late-breaking changes in API Connect and how the delivery of fixes and enhancements are managed.

You have obtained so much knowledge of API Connect over these chapters. Hopefully, you will take this knowledge and apply it to your current organization to make it a rich implementation on your digital transformation journey. Best of luck with your implementation and continued success.

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