Back to Basics: Databases, SQL, and Other Data-Processing Must-Reads

Feeling inspired to write your first TDS post? We’re always open to contributions from new authors.

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. John Leung 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, Natassha Selvaraj’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 Jack Chang 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, Himalaya Bir Shrestha 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, Katia Gil Guzman 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.

Interested in exploring other topics and questions this week? Look no further than these excellent reads:

• In the latest installment of her beginner-friendly “Deep Learning Illustrated” series, Shreya Rao turns to recurrent neural networks and unpacks the intricacies of sequence-based problems.
• For an incisive analysis of the current state of graph and geometric deep learning foundation models, don’t miss Michael Galkin and Michael Bronstein’s excellent deep dive.
• What if, instead of borrowing concepts from neuroscience to develop AI, we turn to AI research to better understand human brains? Stephanie Shen wrote a thoughtful explainer on this emerging topic.
• For anyone looking for a fun and instructive hands-on project to tinker with, we offer Pranav Jadhav’s thorough tutorial on building a GPT model from scratch on a MacBook.
• Stay up-to-date with recent computer-vision research by following along Mengliu Zhao’s accessible recap of two promising papers (on the pixel transformer and ultra-long sequence distributed transformer, respectively).

Thank you for supporting the work of our authors! We love publishing articles from new authors, so if you’ve recently written an interesting project walkthrough, tutorial, or theoretical reflection on any of our core topics, don’t hesitate to share it with us.

Until the next Variable,

TDS Team

Back to Basics: Databases, SQL, and Other Data-Processing Must-Reads was originally published in Towards Data Science on Medium, where people are continuing the conversation by highlighting and responding to this story.