Databricks is lighting the way for organizations to thrive in an increasingly AI-driven world. The Databricks Platform is built on lakehouse architecture, empowering organizations to break down existing data silos, store enterprise data in a single centralized repository with unified data governance powered by Unity Catalog, and make the data available to a variety of user groups to support diverse analytics use cases.

Logging plays a crucial role in app development. As well as debugging everyday issues with the product, a good logger will monitor application behavior and gain insights into user interactions. This is particularly crucial for iOS developers given the frequency of Apple’s official updates and the ferocious competition in the marketplace.

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.