📘 About This Blog

My Certification Roadmap: One Year, Six Milestones 🎯

🗓️ One-Year Certification Plan

Here’s the roadmap I’ve laid out for myself:

1. 🚢 CKAD – Certified Kubernetes Application Developer

  • Goal Date: July 21st
  • Kubernetes is the backbone of modern DevOps and microservices. Mastering it will strengthen my ability to deploy, scale, and troubleshoot containerized applications.

2. 📊 Databricks Data Engineer Associate/Professional

  • Goal Date: September 15th
  • Databricks is transforming how we handle big data and AI workflows. This certification will deepen my knowledge in Spark, Delta Lake, and data engineering at scale.

3. 🤖 AWS + AI OR NVIDIA AI Specialist

  • Goal Date: November 10th
  • I want to validate my cloud AI knowledge either through AWS AI/ML certification or NVIDIA’s Deep Learning Institute track. This one’s all about blending cloud and intelligence.

4. ☁️ GCP Data Engineer OR Azure Developer

  • Goal Date: January 27th
  • Depending on evolving interests, I’ll either focus on Google’s ML-centric data pipelines or strengthen my Azure development skills. Both are highly relevant for enterprise systems.

5. ❄️ Snowflake SnowPro Advanced: Architect

  • Goal Date: March 23rd
  • Snowflake remains core to my data warehousing toolkit. Recertifying ensures I’m up-to-date with new features and best practices.

6. 🔁 Azure Developer OR GCP Data Engineer (Whichever I didn’t take in Jan)

  • Goal Date: May 18th
  • Whichever cloud certification I didn’t take earlier, I’ll complete it here. This will round off my cross-cloud capabilities.

📚 Parallel Learning Tracks

Alongside the certification roadmap, I’m also sharpening my problem-solving and system design skills through daily practice and projects:

  • 🔍 Blind 75 LeetCode – Revisiting core DSA patterns to strengthen my fundamentals
  • 🧮 SQL + Pandas problems – Focusing on real-world data wrangling and analytics
  • 🏗️ System Design – Studying scalable architecture patterns and distributed systems
  • ⚡ Apache Spark Problems – Practicing transformations, RDD/DataFrame operations, and optimizations
  • 🤖 Applied AI Course – Deep diving into ML algorithms, model deployment, and AI workflows
  • 🧪 Kaggle Projects – Applying ML and data engineering to practical datasets and competitions

“Learning never exhausts the mind.” — Leonardo da Vinci