Background jobs are essential to many Ruby on Rails apps. Since the introduction of ActiveJob, Rails developers have been able to manage their background jobs as natively as they do their database records. Still, ActiveJob requires you to select (and support) a backend adapter that will implement ActiveJob's backend. Many use Redis, a memory cache, to queue and process background jobs. Redis comes with incredible speed but is yet another dependency to maintain.

Airports are now heavily inclined towards integrating self-baggage drop facilities to ensure a hassle-free experience and eliminate long-standing queues. Plus, some airports are also introducing smart baggage trolleys to help passengers locate wherever they want to go at just their fingertips. Well, these are just a few of the innovations in the Aviation industry because the list is never-ending. Nevertheless, did you know that the airports using this new technology have seen some amazing results?

This year at Appian World, we announced the launch of Process HQ. This next-generation process mining capability combines both process and business data in a single place, automating process analysis and empowering users with intelligent recommendations for where and how to take action. And because it’s part of the Appian Platform, you can immediately apply these improvements to your processes using our AI automation and workflow orchestration capabilities.

For organizations looking to automate processes for better performance, a combination of RPA and process mining can help. Let’s take a step back to understand why. Automation that’s implemented without data-backed decisions often yields poor results. And data without action leads nowhere. To get the most out of any technology investment, you have to be sure you're using the right combination of tools and techniques.

You can use the new comprehensive search bar in Kong Konnect to quickly retrieve the information you need across Konnect entities so you can focus on taking action. Operating through a platform search API, the new search bar’s query syntax offers advanced and precise search capabilities and is designed to cater to a wide range of search requirements, enabling you to find the information you need across different areas of the product with ease and speed.

In Swift, an enum (short for enumeration) is a powerful feature that allows us to define a data type with a fixed set of related values so we can work with those values in a type-safe way within our code. In this article we’ll be taking a closer look at Swift enums and their applications in Swift, as well as providing some real-world examples of how we could deploy them in our builds.

Node.js 22 has been released, offering a compelling upgrade for developers. It takes over the 'Current' release line, while v21 transitions into maintenance mode until its end-of-life in June. This release delivers advances like a stabilized Watch mode, pattern matching support in the fs module, a default-enabled WebSocket client, and a new--run flag for script execution. It also offers some performance improvements and the usual V8 engine upgrade.

Now that we have set up our API, integrated it with Kong, and connected Stripe with Moesif, we have the infrastructure to begin billing for API usage. Now, we will move into configuring Moesif with the pricing, metering, and access control/governance pieces of the API monetization journey. Let’s kick things off by setting up the prices we want to charge for API usage in Moesif.

Django is a “batteries-included” framework. It has a wide array of built-in features that handle common web development tasks: URL routing, template engine, object-relational mapping (ORM), and database schema migrations. This makes it highly efficient for rapid development, allowing developers to focus on building their applications' unique aspects without reinventing basic functionalities.

"The whole is more than the sum of its parts." Aristotle is credited with this quote, and it's true in the world of data. Legacy systems typically approached their role in a limited manner. Each system was intended to be used by a certain user set and handle well-defined processes and associated data. The result was a disintegrated environment with data being difficult to obtain, and frequently out of date. The parts couldn't easily cooperate to make a whole.