การเปลี่ยนสายอาชีพไม่ใช่เรื่องง่าย โดยเฉพาะเมื่อเราต้องเริ่มเรียนรู้ทักษะใหม่เกือบทั้งหมดตั้งแต่พื้นฐาน แต่มีสิ่งที่เราเชื่อมาตลอดคือ การเรียนรู้จะมีคุณค่ามากขึ้น เมื่อเราแบ่งปันมันให้คนอื่นได้เรียนรู้ไปด้วย และก็โชคชะตาเป็นสิ่งที่เราบังคับไม่ได้ แต่ความพร้อมคือสิ่งที่เราสร้างและฝึกฝนได้เองทุกวัน เมื่อโอกาสนั้นมาถึง

ช่วงนี้เรากำลังเรียน Data Engineer Bootcamp ของ DataSparkTH ซึ่งเป็นคอร์สที่เข้มข้นและครอบคลุมทั้งแนวคิดพื้นฐานและทักษะที่จำเป็นสำหรับการทำงานด้าน Data Engineering และเพื่อเป็นการทบทวนความรู้ของตัวเอง ก็เลยเขียน Learning Notes สรุปบทเรียนแต่ละ Module ลงบน Medium หลังจากเรียนจบในแต่ละช่วง หวังว่าจะเป็นประโยชน์กับคนที่กำลังเริ่มต้นเส้นทางเดียวกันนะ เขียนเป็นภาษาอังกฤษผสมกับภาษาไทย แต่จริงๆคือตั้งใจให้คนไทยอ่านนี่แหละ ในอนาคตคิดว่าอยากฝึกเขียนเป็นภาษาอังกฤษทั้งหมด

"Learning isn't complete when you finish a lesson. It's complete when you can explain it to someone else."

A few months ago, I made one of the biggest decisions in my professional life: to pivot my career into the data field, while keeping an open mind about which specialization to pursue.

Like many career changers, I wasn't coming from a Computer Science background. I had to learn new concepts, new tools, and a completely different way of thinking about data.

There were days when SQL queries looked like another language.

There were evenings when a single JOIN took longer to understand than I expected.

There were moments when I wondered whether I was really capable of becoming a Data Engineer or a Data Analyst.

Yet every difficult concept taught me the same lesson:

Progress in technology isn't measured by how quickly you finish a course, but by how consistently you keep learning.

I'm currently enrolled in the Data Engineer Bootcamp by DataSparkTH, where every module pushes me to think differently—not just about writing SQL queries or designing databases, but about how modern organizations move, transform, and trust their data.

Sharing knowledge

Instead of keeping my notes inside a notebook that nobody would ever read, I decided to do something different. I started documenting everything I learn during the DataSparkTH Data Engineer Bootcamp.

Writing forces me to slow down, organize my thoughts, identify gaps in my understanding, and transform scattered information into knowledge I can explain to someone else.

Writing also forces me to answer difficult questions:

If the answer is yes, then I've actually learned something. If not, I go back and study again. Writing has become my feedback loop.

Many people believe the hardest part of learning Data Engineering is mastering SQL, Python, Spark, or cloud technologies. I disagree. The hardest part is building the habit of continuous learning.

Technology changes rapidly. Documentation evolves. Best practices improve. New tools replace old ones.

The people who succeed are rarely those who memorize everything. They're the ones who know how to learn continuously.

Every article is another opportunity to revisit concepts, simplify complex topics, and reinforce my understanding.

Why Data Engineering?

When people hear "Data," they often think about dashboards, machine learning, or AI. But none of those exist without reliable data.

Behind every recommendation system, business dashboard, fraud detection model, or AI application is a data pipeline built by Data Engineers.

Data Engineering isn't simply about moving data from one place to another. It's about building the infrastructure that enables organizations to make better decisions. That mission resonates with me.

I enjoy understanding systems, breaking complex problems into smaller pieces, and continuously improving processes. The more I learn, the more I realize that Data Engineering is a field where technical skills and analytical thinking meet.

What I've Published So Far

📘 Module 1
Laying the foundations of Data Engineering and understanding the role of modern data infrastructure.
🔗 https://medium.com/@rujira.archive/dataspark-engineer-bootcamp-module-1-5eda308d6713

📘 Module 2
Exploring the principles behind scalable data systems and essential engineering concepts.
🔗 https://medium.com/@rujira.archive/dataspark-engineer-bootcamp-module-2-e278bb9079a6

📘 Module 3
Moving beyond basic SQL into more advanced querying techniques and problem-solving strategies.
🔗 https://medium.com/@rujira.archive/data-engineer-bootcamp-dataspark-module-3-part-2-sql-intermediate-f9149b72544a

📘 Module 4
The role of Python in data engineering—from core fundamentals and control flow to comparing SQL and Python for table creation—while highlighting its integration within Databricks' Medallion Architecture.
🔗 https://medium.com/@rujira.archive/dataspark-engineer-bootcamp-module-4-d14f974110d6

📘 Module 5
This intermediate Python outline covers file path handling, core programming concepts (functions, comprehensions, OOP), error types, and key modules like multi-threading and context managers.
🔗 https://medium.com/@rujira.archive/dataspark-engineer-bootcamp-module-5-0c9a636104be

These articles aren't official course notes. They're snapshots of my personal understanding . (You can share your thought or comments on it)

What's Next

I still have many more modules ahead and more bootcamps! I plan to continue documenting and publishing my learning journey until I complete the bootcamp.

I hope these learning notes will be helpful to:

If you're walking a similar path, I hope my notes help you move one step forward as well. Thank you for following along on this journey. See you in the next module!

Follow my learning journey

📖 Medium — Technical Notes & Learning Articles

🌳Midgard — Technical Notes & Learning Articles

💻 GitHub — Projects & Practice

💼 LinkedIn — Career Journey & Professional Updates