Last summer, I found myself stepping into a role that was both exciting and intimidating: a data analyst that needed to built a data pipeline. Armed with only basic Python skills and no prior coding experience, I had to figure out how to build an end-to-end data transformation pipeline, also known as ETL (Extract, Transform, Load). This involved interacting with APIs, manipulating large datasets, and uploading data to a Snowflake database. The learning curve felt steep, but due to ChatGPT’ I was able to finish the project with succes. The tool I used in this case was the “code copilot” GPT from ChatGPT.
I initially struggled with where to begin. Although I had a foundational understanding of data analysis, I wasn’t familiar with the technical intricacies of building a complete ETL pipeline. However, with the help of ChatGPT, I was able to gradually piece together the process. I would input small tasks and concepts I wanted to tackle, and the AI would provide explanations and snippets of code. This iterative back-and-forth helped me demystify many of the tasks.
By the end of the project, I had created a fully functional ETL pipeline that automated the collection and transformation of data. Without the assistance of generative AI, what seemed like a daunting and nearly impossible task turned into a fulfilling learning experience. It empowered me to stretch beyond my initial capabilities. Generative AI truly served as a valuable tool, transforming what could have been a steep learning curve into a collaborative and enjoyable project.
Pitfalls
While generative AI was incredibly helpful in building my data transformation pipeline, there were some notable limitations. Debugging, for instance, still required significant manual effort. ChatGPT struggled with complex bugs, and I often had to turn to StackOverflow for deeper insights and solutions to fix it myself.
Additionally, technical knowledge was crucial. While the AI helped structure code, I needed to understand APIs, Snowflake, and XML parsing to implement specific details. ChatGPT couldn’t generate the entire solution on its own. ChatGPT is great for creating the global parts of code but I needed to adjust the code most of the time to make it fit in my project.
Moreover, I realized the importance of asking precise questions. You can’t ask the AI to write code without providing clear technical requirements. It’s similar to how a product owner communicates with developers: even if you don’t know how to code, you need to speak their language to convey what you want. In the end, generative AI was a valuable tool, but success depended on my ability to guide it with the right queries.
Thank you for sharing your insightful experience!
It’s inspiring to see how you leveraged ChatGPT to overcome the challenges of building an ETL pipeline with limited coding background. Your story illustrates what generative AI can do in supporting such complex technical tasks, but also defines their limits. Thanks for sharing honestly about your experience with how much debugging requires manual labor and how basic technical knowledge cannot be replaced. Great job of turning what really could have been a burdensome project into a successful learning journey!
Looking back on this journey, how do you think the integration of generative tools like ChatGPT will shape the way people learn and develop technical skills in the future?
I believe the integration of generative tools like ChatGPT will revolutionize how people approach learning technical skills. These tools can accelerate the learning process by providing real-time assistance, allowing users to experiment and tackle projects they might not have considered before. I think AI will act as a powerful supplement, helping people learn faster and with more confidence, but not as a complete replacement for hands-on experience.
Thank you for sharing your experience with building an ETL pipeline from scratch.
It’s so encouraging to see how you used ChatGPT as a “code copilot” to navigate such a steep learning curve, especially with limited prior experience in coding. I appreciate how you balanced highlighting the strengths of using generative AI while also addressing its limitations, particularly when it comes to debugging and the need for technical know-how.
Your point about asking precise questions really resonates—it’s a reminder that even the most powerful tools are only as effective as the instructions we provide. This post is a great example of how AI can empower users to tackle ambitious projects and turn intimidating challenges into meaningful learning experiences.
Keep up the great work!
Really interesting post about your past work experience!
I knew we could use chatGPT to leverage coding but having a real life example is really passionating.
Building such project with AI will probably be the future, as more and more coders will undoubtedly use such AI agent in their daily coding life. However, you showed us that even with a “limited” background in coding, you were able to build an ETL pipeline.
It is also very interesting to see that you point out the limitations of these AI agents, such as the manual debogging and the fact that you still need technical knowledge to accomplish coding tasks.
All in all, it is a very interesting post and a question I’m also curious to know if you noticed any advancements in GPT methods, such as when transitioning from GPT-3 to GPT-4, or from GPT-4 to GPT-4 Turbo. How did these changes impact your work?
I used an older version of GPT (GPT-3 I think) when I did the Python basic course, it was more limited to generating small snippets of code. With GPT-4 (which I used for this project), however, the leap in capabilities is noticeable: it’s much more equipped for handling entire projects, like the ETL pipeline I built. This made a huge difference in how much I could achieve, allowing me to go beyond small tasks and really tackle complex problems with AI support.
Interesting and inspiring post, as someone from a business background and a great interest and IT and coding I can see myself in the same situation when going into a new job. So it is good to see how well GenAI has helped you eventhough it wasn’t without its pitfalls. How would you say your coding skills are now? Are you more confident with it and are you still using the same AI platform or do you have different methods you utilize aswell right now?