I used to be a SQL master. From 2016 to 2020, I spent most of my days crafting complex joins, optimizing queries, and mastering window functions. It was my go-to skill, and I took pride in being able to wrangle even the most unruly datasets.
But fast forward to today, and I barely touch SQL. These days, I find myself working mostly with Python, JSON, or even relying on AI to handle queries for me. It’s a strange feeling, like SQL has quietly slipped into the background of my workflow.
So, is this a trend? Are we witnessing the slow death of relational databases? Or is SQL too deeply ingrained in modern systems to ever truly fade away?
## The Rise of NoSQL and AI
It’s no secret that NoSQL databases have been gaining popularity in recent years. With the rise of big data and real-time analytics, traditional relational databases have struggled to keep up. And then there’s AI, which is increasingly being used to handle complex queries and data processing tasks.
## The Shift in Workflow
For me, the shift away from SQL has been a gradual one. At first, it was just a few Python scripts here and there, but soon I found myself writing more and more code in Python and less in SQL. And when I do need to write SQL, it’s often for simple tasks like data extraction or manipulation.
## Is SQL Still Relevant?
So, is SQL still relevant in today’s data landscape? Absolutely. While AI and NoSQL databases may be taking over certain tasks, SQL remains an essential skill for any data professional. It’s still the best way to extract insights from structured data, and it’s not going away anytime soon.
## The Future of Data Work
But what does the future hold for data work? Will we see a continued shift towards AI-driven data processing, or will SQL remain a staple of the industry? Only time will tell.
## Share Your Thoughts
Are you still writing raw SQL daily, or has it become something you used to be good at but rarely use anymore? Share your thoughts in the comments below!