Portfolio Architecture Examples: Data Engineering Collection
In this latest post about the Red Hat Portfolio Architectures, learn more about the featured collection of data engineering architectures.
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This article is a continuation of a series of posts about our project named Portfolio Architectures. The previous post, Portfolio Architecture Examples: Healthcare Collection, begins with a project overview, introduction, and examples of tooling and workshops available for the project. You may want to refer back to that post to gain insight into the background of Portfolio architecture before reading further.
Data Engineering Collection
The collection featured today is centered around architectures leveraging data engineering concepts and tooling. There are currently eight architectures in this collection and we'll provide a short overview of each, leaving the in-depth exploration as an exercise for the reader.
Cloudlets at Edge
This architecture covers the use case of providing a consistent infrastructure experience from cloud to edge and enabling modern containerized applications at the edge.
(Note: This project is a new architecture and is currently in progress, so I am sharing one of the schematic architecture diagrams and you can monitor this project for updates as it progresses to completion.)
This use case is bringing cloud-like capabilities to the edge locations.
Data Center to Edge
This architecture covers the use case around the data center to the edge where the energy (utility) infrastructure companies operate across a vast geographical area that connects the upstream drilling operations with downstream fuel processing and delivery to customers. These companies need to monitor the condition of pipelines and other infrastructure for operational safety and optimization.
The use case is bringing computing closer to the edge by monitoring for potential issues with gas pipelines (edge).
Edge Manufacturing Efficiency
The manufacturing industry has consistently used technology to fuel innovation, production optimization, and operations. Now, with the combination of edge computing and AI/ML, manufacturers can benefit from bringing processing power closer to data. This helps actions be taken faster on things like errors and predictive maintenance.
The use case is for boosting manufacturing efficiency and product quality with artificial intelligence and machine learning out to the edge.
Edge Medical Diagnosis
This architecture covers edge medical diagnosis in the healthcare industry. It Accelerates medical diagnosis using condition detection in medical imagery with AI/ML at medical facilities.
The use case is accelerating medical diagnosis using condition detection in medical imagery with AI/ML at medical facilities.
Intelligent Data as a Service (iDaaS)
Intelligent DaaS (Data as a Service) is about building and delivery of systems and platforms in a secure and scalable manner while driving data needs for moving towards consumerization in healthcare. Feel free to explore this portfolio-architecture by clicking on the diagram below.
The use case is Intelligent Data as a Service (iDaaS) is about building and delivery of systems and platforms in a secure and scalable manner while driving data needs for moving towards consumerization in healthcare.
An offering of (near) real-time payments lets businesses, consumers, and even governments send and accept funds that provide both available to the recipient and instant confirmation to the sender. Enabling real-time - or at least faster - payments that improve the speed of online payment experiences to customers has the potential to give banks a greater opportunity to win, serve, and retain their customers. By building solutions that capture real-time payment business, banks also can drive higher payment volumes, ideally at lower costs as well as engage new customer segments.
The use case examines financial institutions enabling customers with fast, easy to use, and safe payment services available anytime, anywhere.
Retail Data Framework
Retail is the process of selling consumer goods or services to customers through multiple channels of distribution to earn a profit. A data framework refers to the process of managing enterprise retail data. The framework or system sets the guidelines and rules of engagement for business and management activities, especially those that deal with or result in the creation and manipulation of data.
The use case is creating a framework for access to retail data from customers, stock, stores, and staff across multiple internal teams.
SCADA Interface Modernisation
This Portfolio Architecture targets energy providers in North America that need to be compliant with NERC regulations and in order to achieve this they decide to modernize the interfaces between their business applications and their SCADA systems also for better consumption of information that can be used in combination with AI/ML and decision management tools to better address customer needs.
The use case is providing interfaces with SCADA systems that are compliant with NERC regulations, creating different layers of API gateways to protect business services depending on the network zones.
Additional Portfolio Architecture Solutions
If you are interested in more architecture solutions like these, feel free to export the Portfolio Architecture Examples repository. More architecture collections include:
Published at DZone with permission of Eric D. Schabell, DZone MVB. See the original article here.
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