Adoption of IoT is going to continue to drive an adoption of hybrid cloud. As a result, the solution to these problems cannot simply be providing a managed service for an incredibly complex product. The product itself needs to be simple and easy to use, yet highly scalable, so that it can be deployed and managed directly on the edge and in the cloud with the same level of effort and skill set. In order to continue to drive and support innovation, the data management industry needs to design and build the traditional big data architectures required to consume IoT data and transform that data into valuable insights, identify patterns and make it actionable.
While DevOps is a well known and popular term, DataOps is now emerging as a practice that is of equal importance. DataOps is a blend of data science, DevOps, business intelligence, and data engineering. The goal is to produce agile, actionable, repeatable practices within big data to allow companies to see true value from big data. As data grows exponentially year over year the infrastructure and skills sets to manage that data are becoming more complex. By building competency in DataOps, companies can have groups that can work alongside their existing teams augmenting their existing capabilities pre-emptively.
Organizations around the world are investing enormous amounts of resources in pushing computing to edge devices. There are use cases across most industries like self driving cars, smart grids, healthcare, and many more. These solutions are beginning to take on similar architectural patterns as they evolve from concept to reality. The data value chain is going to move directly to the edge over the course of the next few years as more and more organizations see the need for real-time analytics directly on an intelligent edge.
We hear a lot about the Internet of Things, but what is the Internet of Data? When people talk about the “Internet of Data”, what they are referring to is the collection of data from edge devices and performing deep analysis of that data to gain insights. To truly enable the “Internet of Data”, machine learning and AI processing need to be moved directly to the edge. In order to do this companies need to look for solutions that can handle the entire data value chain directly on the edge and do not create throughput bottlenecks, but use a more democratized architecture.
Microservices have become dominant over the last few years, so much so that it is hard to imagine encountering a modern application built with a SOAP API. The wide spread usage of stateless microservices has allowed for modern applications to be easily and quickly deployed horizontally and directly on the edge. The lightweight nature of REST APIs due to their statelessness, have allowed for applications with less overhead, quicker integration times, and a much more enjoyable programming experience. Microservice adoptions at both the application and middleware layer have driven much of the advances in edge computing.