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Our collective attention has focused so intensely on LLMs in the past year or so, that it’s sometimes easy to forget that the core daily workflows of millions of data professionals are far more likely to involve relational databases and good-old SQL queries than, say, retrieval-augmented generation.
The articles we highlight this week remind us of the need to maintain and grow our skills across the entire spectrum of data and ML tasks, not just the buzziest ones. Taken together, they also make another important point: there’s no clear line separating these kinds of bread-and-butter data operations from the hype-generating, AI-focused ones; the latter often cannot even work properly without the former.
- Simplifying the Python Code for Data Engineering Projects
A strong foundation is key to the success of any complex operation involving large amounts of data. provides concrete advice for ensuring the most basic building block of your data pipeline—the underlying code—is as robust and performant as possible. - How to Learn SQL for Data Analytics
For anyone just taking their first steps in data querying and analysis, ’s latest beginner-friendly guide offers a streamlined roadmap for mastering the most essential elements of SQL in a month; it also devotes a section to helpful pointers for handling SQL problems in the context of job interviews.
- How to Pivot Tables in SQL
As explains, “with a pivot table, a user can view different aggregations of different data dimensions.” Not sure why this matters or how to work with pivot tables in SQL? Jack’s comprehensive resource covers the basics—and then some—in great detail. - Managing Pivot Table and Excel Charts with VBA
Tackling pivot tables from a different angle, presents a hands-on tutorial that shows how you can automate key steps in your work with Excel charts by leveraging the power of VBA (Visual Basic for Applications): “while it could take considerable effort to set up the code in the beginning, once it is set up, it can be very handy and time-saving to analysts who work with numerous large datasets daily.” - Turning Your Relational Database into a Graph Database
While acknowledging the crucial role of relational databases, raises an important point in her debut TDS article: “what if your data’s true potential lies in the relationships between data points? That’s where graph databases come into play.” She goes on to demonstrate how you can transform your relational database into a dynamic graph database in Python.
