Most Glue pipelines start simple and become difficult to manage gradually — formulas get hardcoded, modules grow without boundaries, output files proliferate, and before long a single job is doing too many things in ways that are hard to test, hard to debug, and expensive to change.

Looking back, the bash script wasn't really solving debugging. It was solving prioritization. I just didn't have the vocabulary for it yet.

If you're building a data platform on Azure in 2026, you're going to be asked this question: Azure Databricks or Microsoft Fabric? Both run on Delta Lake, both integrate with ADLS Gen2, both have Spark, and both promise to be your unified data platform. The overlap is real, and the marketing doesn't help.

This post is an honest breakdown of where each genuinely excels, where they overlap, and how to decide without getting lost in feature comparison tables.

Introduction

Before diving into the programmatic details, first some concepts need to be explained. Do realize that in this blog, only the surface is scratched from what is possible with RabbitMQ. A detailed overview can be found in the official RabbitMQ documentation.

There's an idea making the rounds in B2B SaaS product and engineering meetings right now. It sounds reasonable. It feels optimistic. And it's leading companies straight into the same trap they've always fallen into, just at an accelerated rate.

In this tutorial, I'll walk through building a production-grade feature engineering pipeline on Azure Databricks using:

Background Work With Constraints

The new com.codename1.background package schedules work that the OS runs when its conditions are met, mapping to Android JobScheduler and iOS BGTaskScheduler underneath. You describe what the work needs, not when to poll:

In this post, I'll walk through building an agent system that reacts to streaming user behavior in real time using:

The failures that slip through this way share a specific characteristic worth naming directly: they are not failures of the code that was just changed. They are failures in how that code now interacts with something else in the system that was not part of the deployment at all.

Most push-based architectures fail for one reason: they assume traffic is evenly distributed. Production traffic never is. One user may have 50 followers. Another may have 10 million. Designing both scenarios using the same fan-out strategy creates massive operational problems during peak traffic. That is why large-scale platforms evolved from naive push delivery into hybrid push/pull systems optimized around uneven load distribution.

With that one key, the attacker walked straight into the U.S. Department of the Treasury, reset internal passwords, accessed workstations inside the Office of Foreign Assets Control, and read unclassified documents before anyone noticed. The Treasury disclosed it to Congress on December 30. The Department of Justice indicted the alleged operators in March 2025.

Not because the AI model couldn't identify Docker container failure scenarios—it usually could. The failures occurred at the decision boundary: determining when an automated action was appropriate, when escalation was required, and when no action should be taken.

• One has already been tested in lower environments, but is still awaiting business approval
• The other is fully validated and ready to go live

Naturally, you want to release the second feature to production.

That is a useful thing. It is not a platform.

What You Need Before You Start

You need three things to follow along:

Code Patterns in Spring Boot With Kafka

Spring for Apache Kafka gives me two primitives that feel pleasantly old Spring KafkaTemplate for sending and @KafkaListener for receiving. That template/listener model is intentionally similar to other Spring integration tech, which keeps application code focused on domain logic instead of raw client plumbing. 

This was not a standalone exploit. It was a multi-vector convergence where vulnerabilities in the Node/NPM ecosystem, the systemic ungoverned architecture of the Visual Studio Code Marketplace, and the tactical “fog of war” caused by a period of historic GitHub infrastructure instability came together to create the perfect attack.

Azure AI Foundry has a genuinely great portal. You can see your agent runs, the tools it calls, the messages it sends and receives, and even a breakdown of token usage — all in a clean UI.

But here's what actually happens when you're building an agent locally:

The API key was never in its environment.

Introduction

There are many AI models and also many AI coding assistants. Which one to choose is a hard question. It also depends on whether you run the models locally or in the cloud. When running locally, Qwen3-Coder is a very good AI model to be used for programming tasks. In previous posts, DevoxxGenie, a JetBrains IDE plugin, was often used as an AI coding assistant. DevoxxGenie is nicely integrated within the JetBrains IDE's. But it is also a good thing to take a look at other AI coding assistants. In previous blogs, Qwen Code and Claude Code were used in combination with local models.