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Regular expressions, commonly known as regex, are a tool for text processing and pattern matching. In Python, the re module offers a robust implementation of regex, allowing developers to handle complex text manipulation efficiently. In this article, we'll get to grips with regular expressions and provide practical code examples — from the basic to more advanced — so you can understand how to use regex in Python.

This post was originally published on Medium on Nov. 20, 2024. The rise of agentic AI has fueled excitement around agents that autonomously perform tasks, make recommendations, and execute complex workflows blending AI with traditional computing. But creating such agents in real-world, product-driven environments presents challenges that go beyond the AI itself.

Every day, thousands of new apps make their way into the mobile app world. Remember 2018? That’s when app downloads shot up by 9%, and smartphone users spent a jaw-dropping $100 billion on apps. Crazy, right? Now, let’s talk about custom app development and cross-platform apps. By 2018, their market had already crossed $7.5 billion. But why are we going on about 2018?

In recent years, both Docker and Laravel have exploded in popularity. In this step-by-step tutorial, we will dive into how to dockerize an existing Laravel app to run it locally. Then, we'll make it ready to run in a production environment, like for a web server. We will also deploy it to Google Cloud Run without getting into Kubernetes or any YAML configs, taking advantage of our Laravel Docker container. Let’s dig in!

On behalf of Qlik, I’m excited to share some thoughts about our acquisition of Upsolver and how it opens up new possibilities for our customers, helping them achieve their goals in ways previously out of reach. First thing to say is how we’ve been deeply impressed by Ori and the Upsolver team’s relentless focus on innovation and solving customer’s problems. We met over a year ago when our respective teams were looking at ways our technology could jointly work together.

Modern data systems demand flexibility, tool interoperability, and strong data integrity. Legacy formats often create barriers with rigid schemas, inefficient partitioning, and weak transactional guarantees. Apache Iceberg overcomes these limitations with a modular design that decouples metadata from data storage, enabling smooth-schema changes, efficient query pruning, and ACID compliance across engines. This article explores Iceberg’s technical foundations.