Articles

No-Code vs. Low-Code, Turing Completeness, and Conway’s Law

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Man working on machine

No-Code

No-code facilitates the reuse of predefined components, typically using a drag and drop interface or a web form. Such platforms always include things like identity and access management, and most importantly don’t require any code to stitch components together, therefore reducing the need for engineers to spend time architecting databases, APIs, or internal workflows. They are always related to one particular task and audience, like web development, spreadsheets, analytics, market automation, etc. Airtable, Zapier, Webflow, Retool, Waylay Digital Twin solution, and similar apps can be found in this category.

Low-Code

On the other hand, low-code has a different set of goals and user personas in mind. The major misconception about low-code is that the “low” in low-code means that a person with hardly any knowledge of coding is the user of such a platform.

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Using RxJava and Kotlin to Assemble an Android App

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Advanced portable applications accomplish a great deal. They convey information between various back-ends through network APIs. They store and recover information from the local database, process substantial amounts of media, and communicate with web sockets. It’s difficult to monitor all the data from various asynchronous data sources, particularly realizing that the users are used to a simple, fluid user experience.

At the beginning of Android, designers utilized the class AsyncTask from the Android system to accomplish everything outside the primary UI thread. Although async tasks did what they should, utilizing them wasn’t the most charming experience in light of all the standard code you would eventually end up writing due to adaptability issues. If you had a further developed use case (i.e., introducing information from a neighborhood data set on the UI while making an API solicitation to get the most recent information from the backend, refreshing the nearby data set, and introducing the most recent information on the UI), things would quickly spiral. You would wind up with code that is difficult to comprehend and keep up with.

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Defining Event-Driven Architectures: AsyncAPI

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In this article, I am going to be talking about how we can define your event-driven architectures using the AsyncAPI definition.

Introduction

A while ago, I published an article about how you can document REST APIs using Open API 3 specification. This was for synchronous APIs.

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Making the World Become More Programmable With Bill Doerrfeld [Podcast]

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« Make the world become more programmable. »

That’s the goal of Nordic APIs, which holds a series of conferences and events throughout Scandinavia and, more recently, the US, to help organizations make smarter tech decisions and streamline their operations through APIs and strategies. Their work explores the API sector and sheds to light various emerging technologies and trends through their events and blogs.

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Apache Cassandra 4.0: Taming Tail Latencies with Java 16 ZGC

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Like so many others in the Apache Cassandra community, I’m extremely excited to see that the 4.0 release is finally here. There are many, many improvements to Cassandra 4.0. One enhancement that is more important than it might look is the addition of support for Java versions 9 and up. This was not trivial, because Java 9 made changes to some internal APIs that the most performance-oriented Java projects like Cassandra relied on (you can read more about this here).

This is a big deal because with Cassandra 4.0, you not only get the direct improvements to performance added by the Apache Cassandra committers, you also unlock the ability to take advantage of seven years of improvements in the JVM (Java Virtual Machine) itself.

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Client-Side Perf Testing Using Lighthouse CI With Puppeteer

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Whenever we need to do performance testing, mostly it is the APIs that come to mind. 

Testing the performance of an application by putting load on APIs or on servers and checking out various metrics or parameters falls under server-side performance testing.

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Deploying an Apache Kafka mock service with Microcks

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Developers are working with new applications every day using Apache Kafka as the backbone to implement an event-driven architecture (EDA) to support distributed systems. However, this adds new challenges when sharing across teams, even within the same organization. What endpoints are available? What is the structure of the message? That’s why payload examples became critical to speed up development. For this reason, having a reliable and enterprise-grade service to mock Apache Kafka should be an item in your EDA checklist. This post will do a quick review of the Microcks General Availability (GA) version and their support to Kafka.

What is Microcks?

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Coding that sparks joy with Quarkus by Edson Yanaga (@yanaga)

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Does your heart beat faster when you’re developing your Java application? Does it spark joy when you’re coding? If not, then it’s time to leave some old ways behind. Join us on this live coding session and get familiar with Quarkus. You’ll learn to love how your old and new favorite APIs will start in a matter of milliseconds and consume tiny amounts of memory. Hot reload capabilities for development will give you instant sparks of joy! 

These live streamings are powered by xgeeks, a KI group company.

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APIs for All: With Postman’s Arlemi Turpault

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While APIs make it simple to share data back and forth between applications, the process of managing APIs can be anything but simple. A developer working with hundreds of different APIs over the course of their work can get bogged down trying to test, implement, and manage it all, turning what is supposed to be something straightforward into another layer of complexity.

Building API integrations is complex, but Postman makes it easy to create an API service. With Postman, developers can simplify every step of building and managing APIs — from development and testing to collaboration and support — so that more people can design APIs with simplicity, usability, and consistency.

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How to Intercept and Debug All Java HTTP

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Java and the JVM more generally are widely used for services everywhere, but often challenging to debug and manually test, particularly in complicated microservice architectures.

HTTP requests and responses are the core of interactions between these services, and with their external APIs, but they’re also often invisible and inaccessible. It’s hard to examine all outgoing requests, simulate unusual responses & errors in a running system, or mock dependencies during manual testing & prototyping.

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