.png)
1. Google Gemini: A Name That Stuck with Me
Let’s start with Google Gemini.
First of all, the name itself is genuinely cool. “Gemini” implies duality, intelligence, adaptability, and multiple perspectives—qualities that fit perfectly with what modern AI is trying to achieve. On a more personal note, Gemini also happens to be my wife’s astrological sign, which instantly made the name feel familiar and warm rather than abstract or corporate. 😊
From a usage perspective, Gemini feels like Google’s answer to a new generation of AI interactions. It’s not just about answering questions—it’s about reasoning, summarizing, coding, brainstorming, and creating. I found myself using it in three main ways:
- Drafting and refining text
- Exploring technical ideas quickly
- Acting as a thinking partner rather than just a search engine
What impressed me most is how natural it feels to collaborate with Gemini. It doesn’t replace thinking; instead, it accelerates it. That distinction matters.
2. Google AI Studio: Learning by Building
The most fun part of my exploration was Google AI Studio.
I played with the free version, and honestly, it was more powerful than I expected. Rather than just reading documentation, I jumped straight into building small but real projects. This “learn by doing” approach made the experience much more meaningful.
Here are two projects I created during that time:
🔹 Personal Website Revamp
https://yanaihome.com/aiStudio/
I used AI Studio to rethink and rework parts of my personal website. Instead of starting from scratch, I experimented with layouts, content structure, and interaction ideas. AI accelerated the brainstorming phase and helped me move faster from idea to execution.
🔹 Maple Leaf ETF Tracker (For Fun)
https://yanaihome.com/MapleLeafETF/
This was more of a playful experiment—tracking ETFs related to Canadian themes. It wasn’t meant to be production-grade software, but it helped me connect AI tooling with financial data concepts, UI generation, and backend logic. Even “just for fun” projects can teach real skills if you approach them seriously.
The biggest takeaway from AI Studio is this: AI lowers the barrier between an idea and a working prototype. That’s incredibly empowering.
3. Google Developer Platform and Skill Growth
Another pleasant surprise was Google Developer resources, especially skills.google/
The platform is clean, structured, and encouraging. Instead of overwhelming users, it offers guided learning paths that feel achievable—even for someone balancing a full-time job. As I explored it, something unexpected happened: I started thinking seriously about career expansion, not just skill upgrades.
4. Career "Pivot" Strategy: From Backand to Data Engineering
Having worked at the University of Alberta for nearly 20 years, I’ve accumulated something that’s hard to replicate: institutional knowledge. I know how legacy systems work, where the pain points are, and—figuratively speaking—where the “bodies are buried.”
That knowledge is not a liability in the age of AI; it’s an asset.
Rather than a radical career change, what makes sense for me is a pivot—leveraging my backend experience while moving toward data engineering and cloud platforms.
Here’s how I now see the transition:
The Tech Shift: How to Move Forward
Current Skill → New Skill → Learning Path
SQL Queries / Stored Procedures → BigQuery / Snowflake
Learn Analytical SQL: window functions, CTEs, and query optimization at scale.Database Administration → Cloud Architecture
Pursue a GCP Associate Cloud Engineer certification to understand infrastructure, security, and scalability.Manual Data Cleaning → Python (Pandas / PySpark)
Python is the glue of the cloud—connecting data, APIs, transformation logic, and automation.Fixed Schemas → Data Lakes (Parquet, NoSQL)
Learn how to store and process data before the schema is fully defined—a key modern data concept.
This isn’t abandoning what I know; it’s extending it into environments where AI and data naturally live.
The Tech Shift (The "How")
| Current Skill (Backend) | New Skill (Data Engineering) | Learning Path |
| SQL Queries / Stored Procs | BigQuery / Snowflake | Learn "Analytical SQL" (Window functions, CTEs). |
| Database Administration | Cloud Architecture | Get a "GCP Associate Cloud Engineer" cert. |
| Manual Data Cleaning | Python (Pandas/PySpark) | Python is the "glue" of the cloud. |
| Fixed Schemas | Data Lakes (NoSQL/Parquet) | Understand how to store data before it has a schema. |
Final Thoughts
What began as playful experimentation during a holiday break turned into real inspiration. Google AI tools—Gemini, AI Studio, and Developer learning paths—did more than showcase technology. They reminded me that learning doesn’t stop with seniority, and careers don’t have to follow a straight line.
Sometimes, all it takes is curiosity, a bit of free time, and the willingness to build something—even “just for fun.”
2026 suddenly feels a lot more interesting.
No comments:
Post a Comment