Reviewing MongoDB Data Workload Migrations

Over the last few years I’ve worked on and led a number of workload projects related to various databases. MongoDB is one of those databases. With some of the ongoing questions I’m asked I found myself wanting to review what the current options are for workload migrations to Mongo DB. Are there new options, is it still the same host of options I’ve reviewed many times before? I wanted to know, so this post is my quick list of findings.

Migrating database workloads isn’t just about moving data it’s about rethinking how your application interacts with data. Depending on your source system and reqs, you can choose from several strategies. These may address not only data migration but also the accompanying application logic, query patterns, and operational practices. Here’s an overview of both popular and lesser-known methods that seem to be the recent, current, and ongoing options:

Continue reading “Reviewing MongoDB Data Workload Migrations” →

Writing Swift Like a Pro: Clean Code, Separation of Concerns, and AI-Assisted Development

Swift is a powerful, multi-paradigm language that blends object-oriented programming (OOP), protocol-oriented programming (POP), and functional programming (FP). With the rise of artificial intelligence (AI) tools such as GitHub Copilot, Cursor, and Tabnine, it has become easier than ever to generate code quickly. However, while AI accelerates development, it does not replace software craftsmanship. If you already have strong coding practices and discipline, AI can be a valuable assistant. If you rely on AI blindly, you risk generating unreadable, unmaintainable, and inefficient code at scale.

Writing high-quality Swift code requires discipline in applying good naming conventions, maintaining a clear separation of concerns (SoC), following the five principles of SOLID design (Single Responsibility, Open/Closed, Liskov Substitution, Interface Segregation, and Dependency Inversion), and ensuring maintainability through best practices. While AI-generated code can be useful, it must be refactored and reviewed for clarity and correctness.

Continue reading “Writing Swift Like a Pro: Clean Code, Separation of Concerns, and AI-Assisted Development” →

Swift Coding Conventions: Writing Maintainable, Readable, and Elegant Code

Swift – Apple’s slick and safety-focused (? still not sure I’m bought in on that phrasing) programming language – has become the backbone of Apple’s development. Whether you’re hacking on an indie app or shipping production-grade enterprise software, writing consistent, readable, and idiomatic Swift is non-negotiable. Swift has a robust set of guidelines to follow, and I wrote a thing or three about it’s style of OOP, POP, and FP rolled into one language here, but let’s cut through the noise and hit the key conventions that will level up your Swift code.

Continue reading “Swift Coding Conventions: Writing Maintainable, Readable, and Elegant Code” →

Swift: A Multi-Paradigm Powerhouse—OOP, POP, and FP

Swift is a versatile toolkit that lets you solve problems in multiple ways. Whether you’re leveraging the familiar object-oriented principles, diving into Swift’s protocol-oriented magic, or harnessing the power of functional programming, Swift has you covered. In my latest swift dive I’ve put together a break down of these paradigms per Swift usage.

Continue reading “Swift: A Multi-Paradigm Powerhouse—OOP, POP, and FP” →

CAP Theorem Insights for Apache Kafka and Flink

In this article, I’ll explore CAP Theorem and its implications on distributed systems, particularly focusing on Apache Kafka, Apache Flink, and Apache Cassandra. I’ll then dissect how CAP influences these systems in real-world scenarios, delve into some of the edge cases like split-brain scenarios, and offer actionable strategies to mitigate challenges. Finally, a wrap up with deployment strategies for self-hosted environments and discuss how Confluent Cloud tackles CAP-related challenges.

What is the CAP Theorem?

The CAP Theorem, introduced by Eric Brewer, states that in a distributed data system, you can only guarantee two out of the following three properties:

  • Consistency (C): Every read receives the most recent write or an error.
  • Availability (A): Every request receives a response, even if it’s not the most recent write.
  • Partition Tolerance (P): The system continues to function despite network partitions.

This means that distributed systems inherently make trade-offs, and understanding these trade-offs is key to designing robust architectures.

CAP Theorem and Apache Kafka/Flink

Continue reading “CAP Theorem Insights for Apache Kafka and Flink” →