Agentic data science with the Data Agent Kit
cloud.google.com·8h ago
Data Science
Simon Willison's Blog·Jul 29·5 min read
Quoting D. Richard Hipp
TL;DR
D. Richard Hipp reflects on the evolution of data querying from COBOL programming to SQL, highlighting how SQL simplified the process for developers.
✦ Why It Matters
Consider learning SQL to enhance your data querying skills and adapt to evolving programming roles.
Key Takeaways
- Learn SQL to streamline data querying processes.
- Understand the evolution of programming roles in data management.
- Adapt to new technologies to stay relevant in your field.
- Focus on higher-level programming tasks as automation increases.
Full Summary
In the past, querying large datasets required specialized COBOL programmers who wrote extensive code to handle data requests. The introduction of SQL (Structured Query Language) revolutionized this process by allowing users to specify their data queries in a much simpler format.
This shift meant that the complex code generation previously done by COBOL programmers could now be automated, making data access more efficient. However, the role of programmers evolved rather than disappeared; they began focusing on higher-level tasks and system design.
This transition illustrates how advancements in technology can change job functions while still requiring skilled professionals. The implications of this shift are significant for software development and data management, as it emphasizes the need for continuous learning and adaptation in the tech industry.