Rodrigo Ribeiro’s Post

🌟 Shoutout to Brian Meeker for rocking Stir Trek this year! 🌟 We’re super proud of Brian, who gave an awesome talk sharing how our team tackled a big scaling problem with some clever tech solutions, breaking down our new data processing pipeline and how we made our system more flexible for the future. Even if you don’t have a software development background, Brian’s insights are super interesting and definitely worth a watch! You can see the full session title, description, and link below. Messaging With Limits: Concurrent, Multi-Stage Data Processing in the Real World -- Our team at GetThru found ourselves with a scaling problem. Working with our existing infrastructure for sending text messages was becoming increasingly complex. Years of bewildering external requirements had led us to the point where the existing system was both incorrect and difficult to reason about. In this session, we'll go over a case study of our new concurrent, multi-stage data processing pipeline. Starting from the fundamentals, we'll see how our team efficiently handled our existing external requirements and built in flexibility for the future. Finally, we'll look at how we did all of this in our existing monolith, avoiding the complexity of microservices.

I've been having so many thought-provoking conversations on the AI-fueled collision between data, analytics and software engineering. As these once-distinct markets increasingly overlap, each approaches the challenges from very different perspectives. If you haven't yet, check out my latest research: https://lnkd.in/etJqXV4f

Brilliant content for anyone in the Data Industry as well.

I post about technical data science concepts, my journey in tech, and topics affecting people in the software and data science industries. Follow if you like my content.

Check out my part 2 article "The Observability Stack" on Medium.

Stacks: The simplest yet most powerful data structure, following the LIFO principle ⬆️⬇️. Push data in, pop it out, and repeat! 🔄. From managing function calls to solving complex algorithms 🧩, stacks save the day!

As a software engineer, we need to review product data in specific scenarios to ensure feature effectiveness, performance, and user satisfaction. But here's my experience declaring 'What' and 'How' from a data point of view. Let me know your tips on consuming data POV in the comments below!

So, this week, three experienced leaders in tech shared something eye opening for me... And it wasn't about anything complex or really challenging. All of them had to silently learn on the job how to read a plot that is literally everywhere. In fact, they didn't feel super comfortable explaining how to make or read one either. I'm talking about a Histogram. It's used in like every analysis, every dashboard, every quant discovery project, ... But this fundamental skill I take for granted (given a data background), may not be as widely known as I thought. So, my mission for this week is to stop everyone from suffering in silence! If you work with data (hello every product team in the world 👋 ), take 5 minutes to learn about the humble Histogram in this video or hit 👍 so your team knows. Please! I care about you... and your data skills. So tell me what else you want to learn? 😉

🎥 Dive into the essentials of data management in software development with our latest video. Adam Cogan and Matt Wicks from SSW seasoned developers, share their expertise on maintaining data integrity and more. 🔍 Topics Covered: - Cascade & soft deletes - Handling UI and data bugs - Real-world applications of data principles 👨💻 Whether you're a budding developer or looking to refine your coding practices, these insights are invaluable. Don't miss out! 👉 Watch now: https://lnkd.in/gxREBc8p #DataManagement #SoftwareDevelopment #Coding #TechTalk #DeveloperInsights

Wondering if the LinkedIn Premium upgrade is worth it? 🤑 Check out this breakdown of how we match job seekers most effectively. Kudos to the Machine Learning + Data Science teams involved! https://lnkd.in/gSWWQUf2