Chapter 5: Ingesting and Streaming Data from the Edge

Edge computing can reduce the amount of data transferred to the cloud (or on-premises datacenter), thus saving on network bandwidth costs. Often, high-performance edge applications require local compute, storage, network, data analytics, and machine learning capabilities to process high-fidelity data in low latencies. AWS extends infrastructure to the edge, beyond Regions and Availability Zones, as close to the endpoint as required by your workload. As you will have learned in previous chapters, AWS IoT Greengrass allows you to run sophisticated edge applications on devices and gateways.

In this chapter, you will learn about the different data design and transformation strategies applicable for edge workloads. We will explain how you can ingest data from different sensors through different workflows based on data velocity (such as hot, warm, and cold), data variety (such as structured and unstructured), and data volume (such...