Lunar Eclipse, Canadian Steel, and the Unbroken Database: An Agile Odyssey

If it ain’t broke, iterate it anyway. That’s the Agile mantra we chant while sipping lukewarm office coffee, right? Because, let’s face it, ‘perfect’ is just a pre-alpha version of ‘slightly less broken.’ Think of it as existential software development. We’re all just debugging the human condition, one sprint at a time.

Speaking of software development, let’s talk SQL, since I’ve just learnt it. Ah, SQL. The language of the gods, or at least the gods of data. Is it just for tables and joins? SQL is the existential poetry of the digital age. It’s how we ask the universe, “Why are there so many null values in my life?” and get back a neatly formatted table. It’s not just data; it’s the philosophical backbone of every e-commerce website selling artisanal beard oil.

Now, let’s pivot to the geopolitical theatre of the absurd. Canadian steel. Is it that good? Or is it just that everyone’s got a crush on the new PM, since the last one turned out to be a little weird? It’s like when a band gets a new drummer and suddenly their old songs sound amazing. Is it the drummer, or are we just experiencing a collective, temporary delusion? Maybe the steel is just really, really polite. ‘Excuse me, would you mind being a bridge girder?’ Or perhaps, it’s less politeness and more of a cosmic suggestion box situation. ‘Dear Universe, please make this bridge not wobble like a caffeinated chihuahua on roller skates. Sincerely, Commuters.’ And the steel, being a diligent employee of the cosmos, just shrugs and goes, ‘Fine, but I’m charging extra for the existential dread reduction.’ Because let’s face it, bridges built with polite steel are still bridges, and bridges are just a continuation of a road for cars, paved with the hopes and dreams of people who are late for work. And if that steel isn’t polite, who knows, maybe it’ll just decide to become a giant, sentient paperclip and hold the whole city together with sheer, metallic spite. You wouldn’t want that, would you? Think of the traffic. So, back to Canadian steel… is it that polite? Does it whisper sweet nothings to the architectural blueprints? Does it offer to hold your hand during particularly windy days? Because a 50% tariff suggests it’s either the most courteous metal on Earth, or someone’s having a very expensive, very shiny laugh.

And while we’re pondering the mysteries of the universe, there’s a lunar eclipse happening. You know, that cosmic event where the Earth decides to play shadow puppet with the moon. It’s like a celestial PowerPoint presentation on the fragility of existence. Where can you see it? Well, not from your office cubicle, or your computer screen. Get out there. Find a hill, a rooftop, a really tall stack of old sticky notes. Gaze up and ponder the infinite abyss. Is it just a shadow? Or a metaphor for the ever-growing backlog of life?

Maybe the lunar eclipse is just a giant, cosmic bug. Maybe it’s a feature, not a bug? Who knows? That’s the beauty of Agile, right? We’re all just figuring it out as we go, iterating our way through the darkness, hoping we don’t deploy a critical error to production at 4pm on a Friday.

So, next time you’re stuck in a sprint planning meeting, debating the merits of Canadian steel or wondering if SQL is secretly sentient, remember the lunar eclipse. We’re all just tiny specs in a vast, ever-changing universe. And if it ain’t broke, iterate it anyway. Because, in the grand scheme of things, what’s a few more bugs?

From Zero to Data Hero: My Google Data Analytics Journey

Just a few short months ago, the world of data analytics felt like a vast, uncharted ocean. Now, after completing Google’s Data Analytics Professional Certificate (or at least the 12+ modules that make up the learning path – more on that later!), I feel like I’ve charted a course and am confidently navigating those waters. It’s been an intense, exhilarating, and sometimes head-scratching journey, but one I wouldn’t trade for anything.

My adventure began in October 2024, and by February (this week) 2025, I had conquered (most of) the learning path. Conquer is the right word, because it was definitely an intense learning curve. 2000’s dev junior SQL skills? Yeah, they got a serious dusting off. And my forgotten Python, which was starting to resemble ancient hieroglyphics? Well, let’s just say we’re on speaking terms again.

The modules covered a huge range of topics, from the foundational “Introduction to Data Analytics on Google Cloud” and “Google Cloud Computing Foundations” to more specialized areas like “Working with Gemini Models in BigQuery,” “Creating ML Models with BigQuery ML,” and “Preparing Data for ML APIs on Google Cloud.” (See the full list at the end of this post!) Each module built upon the previous one, creating a solid foundation for understanding the entire data analytics lifecycle.

But the real stars of the show for me were BigQuery and, especially, Looker Studio. I’ve dabbled with other data visualization tools in the past (mentioning no names… cough Microsoft cough Tableau cough), but Looker Studio blew me away. It’s intuitive, powerful, and just… fun to use. Seriously, I fell in love. The ease with which you can connect to data sources and create insightful dashboards is simply unmatched. It’s like having a superpower for data storytelling!

One of the biggest “aha!” moments for me was realizing the sheer power of data insights. Mining those hidden gems from large datasets is incredibly addictive. And the fact that Google makes it so easy to access public datasets through BigQuery? Game changer. It’s like having a data goldmine at your fingertips.

This learning path has ignited a real passion within me. So much so that I’m now pursuing a Data Analysis Diploma, which I’m hoping to wrap up before June. And, because I apparently haven’t had enough learning, I’m also signing up for the Google Cloud Data Analytics Professional Certificate. I’m all in!

I have to say, the entire Google Cloud platform just feels so much more integrated and user-friendly compared to the Microsoft offerings I’ve used. Everything works together seamlessly, and the learning resources are top-notch. If you’re considering a career in data analytics, I would wholeheartedly recommend the Google path over other options.

I’m especially excited to dive deeper into the machine learning aspects. And the integration of Gemini? Genius! Having it as a code buddy has been a huge help, especially when I’m wrestling with a particularly tricky SQL query or trying to remember the correct syntax for a Python function. Seriously, it’s like having a data analytics guru by my side.

Stay tuned for future posts where I’ll be sharing more about my data analytics journey, including tips and tricks, project updates, and maybe even some data visualizations of my own!

Coursera do an official course = https://www.google.com/url?sa=E&source=gmail&q=https://www.coursera.org/professional-certificates/google-data-analytics – this you get a recognised formal professional certificate.

Or jump into Google Cloud Skills Boost: https://www.cloudskillsboost.google/ and get yourself a Cloud account and friendly with Gemini.

Modules Completed:

  • Work with Gemini Models in BigQuery
  • Analyzing and Visualizing Data in Looker Studio
  • BigQuery for Data Analysts
  • Boost Productivity with Gemini in BigQuery
  • Create ML Models with BigQuery ML
  • Derive Insights from BigQuery Data
  • Developing Data Models with LookML
  • Google Cloud Computing Foundations- Data, ML, and AI in Google Cloud
  • Introduction to Data Analytics on Google Cloud
  • Manage Data Models in Looker
  • Prepare Data for Looker Dashboards and Reports
  • Prepare Data for ML APIs on Google Cloud