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This article is the first in a series introducing the architecture and solution to accelerate machine learning model training. The next article compares traditional solutions and explains how this new approach differs.
With artificial intelligence (AI) and machine learning (ML) becoming more pervasive and business-critical, organizations are advancing their AI/ML capabilities and broadening the use and scalability of AI/ML applications. These AI/ML applications require data platforms to meet the following specific requirements:
In our previous article from this series we talked about the logical common architectural elements found in an intelligent data as a service (iDaaS) solution for the healthcare industry.
The process was laid out how we approached the use case and how portfolio solutions are the base for researching a generic architecture. It continued by laying out the process of how we approached the use case by researching successful customer portfolio solutions as the basis for a generic architecture.
It's that time of year again when we get the Red Hat Summit 2022 call for papers!
This year seemed to be a perfect time to go all in with sessions around our architectures based on a series of talks we've designed to showcase the various aspects we cover. Some are vertical aligned and others are just customer domains, but all of them include extensive research into how to implement successful architectures at scale.
There is a bias toward backend engineers at the principal engineer level. This leaves frontend engineers heavily disadvantaged when it comes to promotion time.
When someone says they’re a fullstack engineer, what they usually mean is one of two things: 1) They’re a coding bootcamp grad that has minimal experience with Node.js and an emphasis on the frontend. Or, 2) they’re an experienced backend engineer that knows a little bit of HTML and CSS. A truly fullstack software engineer is a rare find.
In the previous article from this series we introduced an architecture around intelligent data as a service (iDaaS) for the healthcare industry.
The process was laid out how we approached the use case and how portfolio solutions are the base for researching a generic architecture.
If you do a meta analysis on constructs such as SOLID, or any other architectural best practices, you'll easily see how there's another abstraction layer above these principles, arguably being the foundation of everything you do, which is KISS. KISS of course implies ...
Keep It Stupid Simple
One of the definitions used to approach software architecture is that it is responsible for defining the parts of a project and the technological strategy. And, like any strategy, there must be an ongoing process of review and update. That's why, here on the Zup Open Source team, we're using the C4 model.
Having a view of your project's architecture is crucial in several aspects, as it helps you answer questions such as: how can my system integrate internally or with other systems? Or, how can I guarantee security between my applications? And so on.
I mentioned previously that I had submitted some content to DevConf.CZ 2022 with a focus on the architecture work I've been doing lately.
What's DevConf.cz?
Numerous enterprise solutions based on the microservices architecture have an issue with generalizing event flow from different sources. A lot of solutions also have various providers, for example:
Presto (PrestoDB and Trino) is a popular choice for organizations to run interactive analytic queries on multiple data sources at a large scale. Positioned as SQL-on-Everything, Presto is designed to be a storage-independent engine that queries against any disparate data sources anywhere.
Many organizations are constantly modernizing their data platforms and developing scalable solutions to fit their existing and future needs. Although Presto is proven to have massive data processing capabilities, it is not optimized while dealing with data access across the pipeline. As a result, data platform engineers still have to find extra solutions to eliminate data redundancy, error-prone, slow and inconsistent performance, and high costs.
Historically, in monolithic architectures, logs were stored directly on bare metal or virtual machines. They never left the machine disk and the operations team would check each one for logs as needed.
This worked on long-lived machines, but machines in the cloud are ephemeral. As more companies run their services on containers and orchestrate deployments with Kubernetes, logs can no longer be stored on machines, and implementing a log management strategy is of the utmost importance.
In my tenure as a solution architect in financial services working for a global consulting firm, I have often questioned the best way to practice enterprise architecture. A common challenge that many architecture consultants face is that most client firms insist on using their proprietary enterprise architecture content. I have observed that frequently such architecture content does not always distinguish between the solution and software architecture.
This article is not an academic paper. In this article, I present my experiences and ideas to help my colleagues better understand the vague differences between two similar but distinct architecture disciplines. At the very least, this article may trigger a conversation via the comments section that may persuade me that I am wrong.
How awesome would it be to find top trending articles in the Microservices Zone in one place so that you can always stay up to date with the latest trends in technology? We dug into Google analytics to find the top 10 most popular Microservices articles in August. Let's get started!
Enforcing rate limits on microservices is a common requirement in the API economy. In this article, learn how to build a custom rate limiting solution. Follow this step-by-step guide to rate-limiting your microservices.
DevConf.US 2021 the 4th annual, free, Red Hat sponsored technology conference for community project and professional contributors to Free and Open Source technologies was a few weeks ago.
It was a lot of fun to share some of the below workshop experiences with the attendees virtually. Some of the fun I will share here as, believe it or not, I hosted the workshop online from the lovely island of Rhodes, Greece.