Businesses today rely heavily on data to inform decisions, predict trends, and optimize operations. However, more data volume and complexity has led to growing pressure to find scalable, cost-effective solutions for data storage while staying within IT budgets. Companies want to handle both structured and unstructured data efficiently, while supporting advanced data analysis and machine learning use cases.

Kotlin was built to replace Java. Or at least, supersede it for a wide range of Android development tasks. Released by JetBrains in 2016, it is designed to address some of Java’s drawbacks and provide a smoother, faster alternative for devs everywhere. So really the question in the title is redundant, right? Kotlin is just better, surely? Well, actually it’s much more nuanced than that.

The relentless rise of ChatGPT and other Large Language Models has brought a major breakthrough in the world of artificial intelligence. With key industry players like Meta, Google, Nvidia and Microsoft competing fiercely to dominate this space, it’s likely we’ve only seen the start of the innovation. Several big companies have made their AI Models and platforms available for the public to use and build on.

Today’s enterprise networks are complex. Potential attackers have a wide variety of access points, particularly in cloud-based or multi-cloud environments. Modern threat hunters have the challenge of wading through vast amounts of data in an effort to separate the signal from the noise. That’s where a security data lake can come into play.

Since it was created by Facebook in 2012 and made publicly available in 2015, GarphQL has changed everything about how we fetch data from servers for our front-end apps. Most front-end clients typically use REST APIs to retrieve data from the server, this includes mobile apps for platforms like Android, iOS, and Flutter, as well as JavaScript frameworks like React, Angular, Vue, and Next. A huge advantage of GraphQL is that it enables front-end clients to request only the API they require.

Logging is a crucial practice that helps developers monitor, debug, and maintain their applications. Python, a versatile and widely used programming language, provides a comprehensive logging module that makes it easy to integrate logging into your applications. This post introduces Python logging, covering the basics, various logging levels, practical implementations, and more.

A sub-discipline of artificial intelligence (AI), machine learning (ML) focuses on the development of algorithms to build systems capable of learning from, and making decisions based on, data. In iOS development, ML allows us to create applications that can identify patterns and make predictions, adapting a user’s experience by learning from their behaviour.

Started in 2009 as a research project at UC Berkeley, Apache Spark transformed how data scientists and engineers work with large data sets, empowering countless organizations to accelerate time-to-value for their analytics activities. Apache Spark is now the most popular engine for distributed data processing at scale, with thousands of companies (including 80% of the Fortune 500) using Spark to support their big data analytics initiatives.

In the last article, we gave a general introduction to using Swift for backend development. We discussed its advantages: its robustness, stability and, above all, its simplicity. Now we’re going to drill a little deeper, and provide some best practices that will help you in your day-to-day work. Our work today will focus on Vapor, an open-source web framework that provides a robust foundation for websites, APIs and Cloud projects.