Welcome to another chapter in the ongoing series I started covering my journey into the world of cloud-native observability. If you missed any of the previous articles, head on back to the introduction for a quick update.
After laying out the groundwork for this series in the initial article, I spent time sharing who the observability players are, looked at the ongoing discussion around monitoring pillars versus phases, shared thoughts on architectural level choices being made, and shared the open standards available within the open source landscape. I continued onwards with a few of the architectural challenges you might encounter when older monolithic applications and monitoring tools are still part of an organization's infrastructure landscape. Finally, I walked you through the open-source visualization and dashboard project called Perses by introducing my hands-on workshop.
Today's software applications are getting more complicated; thus, every testing team needs to focus on expanding test coverage. To achieve this goal, it is important to use a combination of testing types, such as unit testing, integration testing, system testing, and end-to-end testing, depending on the software application's complexity and requirements.
End to End (E2E) testing is designed to ensure that all components of a software application are working together correctly and that the system as a whole meets the desired functionality, performance, and reliability requirements.
Have you ever wondered how to create visually appealing and consistent emails that leave a lasting impression on your recipients? That's where template-based email design comes in.
Template-based email design involves using pre-designed templates as a foundation for your email campaigns. These templates provide a framework that ensures consistency in branding, layout, and overall design.
As cloud computing continues to gain popularity, more and more organizations are adopting cloud-based data solutions to manage their ever-growing data needs. One of the most popular data integration services in the cloud is Azure Data Factory, a fully managed, cloud-based data integration service that allows you to create, schedule, and orchestrate ETL and ELT workflows.
With the increasing demand for Azure Data Factory expertise, it's no surprise that experienced professionals in the field are in high demand. If you're looking to land a job as an Azure Data Factory expert or simply looking to improve your knowledge in the area, it's important to be well-prepared for the interview process.
In earlier blogs, we saw many languages like Python, C++, Java, and so on that developers use to develop applications that we use to retrieve and insert data. In this blog, we shall learn about RAML which is a language used to create API specification.
Introduction to RAML
RAML stands for RESTful API Modelling Language. It is a YAML /JSON-based modeling language. The language is simple and structured, which is readable by both humans and computers. We use HTTP methods to call the resources while creating API specifications.
The DRY (Don't Repeat Yourself) principle is a fundamental concept in software engineering that promotes code reuse. It states that if you have written a piece of code, then it should be reused throughout the codebase rather than repeated somewhere else. It advocates for avoiding duplication of code or logic and encourages developers to strive for a single source of truth. By adhering to DRY, you'll get the following benefits:
Improved Code Quality: Duplicated code is prone to inconsistencies. By adhering to the DRY principle, developers ensure that code is written once and reused whenever needed, which greatly improves the quality of the system.
Time and Effort Savings: Duplicated code requires redundant efforts. When changes or updates need to be made, developers have to modify the code in multiple places, which consumes time and also increases the risk of introducing errors. The DRY principle helps save valuable time and effort by centralizing code logic.
Before we start, I would definitely recommend getting familiar with the definition and benefits of Event Sourcing. Sara did an amazing job in explaining what Event Sourcing is in more detail and comparisons. Let's briefly touch upon some Event Sourcing aspects that are going to be important for this article.
In order to validate a business rule and/or make any decision in our software, we need to create a model representation of our domain. For the purposes of this article, I'm going to call this model the Decision Model. There are different ways of storing this model durably.
Apparently, hot-cold data separation is hot now. But first of all:
What Is Hot/Cold Data?
In simple terms, hot data is the frequently accessed data, while cold data is the one you seldom visit but still need. Normally in data analytics, data is "hot" when it is new and gets "colder" and "colder" as time goes by.
The world is what we make it. Tech — and AI — follow the same principles.
On this week's episode of Dev Interrupted, we sit down with Christina Enchevta, a Director of Engineering at GitHub, to unravel the link between the values we hold and the things we build. We delve into how AI applications mirror our values, intentionally or not, and how this can lead to surprising outcomes, no matter how benevolent our intentions are.
Upgrading to Jakarta EE 10 from an older version of Jakarta EE or Java EE can be a bit tricky and may require some extra attention to detail. One of the main things you may encounter is making sure your existing code and libraries are compatible. Some libraries may still be using the javax package, which can cause conflicts when trying to run your applications on a Jakarta EE server like Eclipse GlassFish 7. You might also run into problems with some deprecated APIs that were removed in Jakarta EE 10.
But don't worry; there are automation tools to help you with the upgrade and examples of how to use them, which I created in the OmniFish GitHub repository.
When you start learning AWS, you should have a clear understanding of networking essentials. After you get an AWS account, AWS creates a default network space for you. Either you can use that or if you have a different use case, you have to configure different networking components under AWS.
This article will guide developers to start their journey on AWS by introducing networking fundamentals with easy-to-understand examples. After you go through the article, I am sure that you will be in a good spot to tackle different networking challenges faced in your career.
Neal Ford is Director, Software Architect, and Meme Wrangler at Thoughtworks, a software company and community of passionate, purpose-led individuals who think disruptively to deliver technology to address the most demanding challenges, all while seeking to revolutionize the IT industry and create positive social change. Neal is an internationally recognized expert on software development and delivery, especially at the intersection of Agile engineering techniques and software architecture. He has authored magazine articles, nine books (and counting), dozens of video presentations, and spoken at hundreds of developers' conferences worldwide. Neal's topics include software architecture, continuous delivery, functional programming, and cutting-edge software innovations, and he includes a business-focused book and video on improving technical presentations.
Apiumhub had the opportunity to do an interview with Neal Ford, a Global Software Architecture Summit speaker, to get to know what metrics he normally uses and learn more about his chapter in the Software Architecture Metrics book that was recently published by O'Reilly.
Scalability testing is an approach to non-functional software testing that checks how well applications and infrastructure perform under increased or decreased workload conditions. The organization can optimize infrastructure costs and create the best user experience by determining server-side robustness and client-side degradation.
It makes it easier to fix defects and ensure software applications' flawless functioning. As part of the testing process, testers usually draft test scenarios that force the system to meet new requirements.
Database sharding is a powerful technique employed to manage large databases more effectively. It involves partitioning a large database into smaller, more manageable parts, known as shards. The term "shard" signifies a small fragment of a whole, aptly describing this method of breaking down a large database into smaller, more manageable pieces. Sharding is often applied to databases for several reasons, including improving query performance, facilitating data organization, and enhancing scalability. By distributing data across multiple servers, sharding can significantly reduce the response time for data queries, provide a more organized data structure, and allow for easier scaling as the amount of data grows.
The above diagram presents a visual representation of a sharded database. The primary database is segmented into smaller shards, each of which is stored on a distinct server.
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