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


Feeling inspired to jot down your first TDS post? We’re at all times open to contributions from recent authors.

Our collective attention has focused so intensely on LLMs previously 12 months or so, that it’s sometimes easy to forget that the core day by day workflows of hundreds of thousands of information professionals are much more prone to involve relational databases and good-old SQL queries than, say, retrieval-augmented generation.

The articles we highlight this week remind us of the necessity to take care of and grow our skills across your complete spectrum of information and ML tasks, not only the buzziest ones. Taken together, additionally they make one other vital point: there’s no clear line separating these sorts of bread-and-butter data operations from the hype-generating, AI-focused ones; the latter often cannot even work properly without the previous.

  • Simplifying the Python Code for Data Engineering Projects
    A robust foundation is vital to the success of any complex operation involving large amounts of information. provides concrete advice for ensuring probably the most basic constructing block of your data pipeline—the underlying code—is as robust and performant as possible.
  • The way to Learn SQL for Data Analytics
    For anyone just taking their first steps in data querying and evaluation, ’s latest beginner-friendly guide offers a streamlined roadmap for mastering probably the most essential elements of SQL in a month; it also devotes a bit to helpful pointers for handling SQL problems within the context of job interviews.
Photo by Benoît Deschasaux on Unsplash
  • The way to Pivot Tables in SQL
    As explains, “with a pivot table, a user can view different aggregations of various data dimensions.” Unsure why this matters or easy methods to work with pivot tables in SQL? Jack’s comprehensive resource covers the fundamentals—after which some—in great detail.
  • Managing Pivot Table and Excel Charts with VBA
    Tackling pivot tables from a special angle, presents a hands-on tutorial that shows how you may automate key steps in your work with Excel charts by leveraging the ability of VBA (Visual Basic for Applications): “while it could take considerable effort to establish the code at first, once it is ready up, it might be very handy and time-saving to analysts who work with quite a few large datasets day by day.”
  • Turning Your Relational Database right into a Graph Database
    While acknowledging the crucial role of relational databases, raises a very important point in her debut TDS article: “what in case your data’s true potential lies within the relationships between data points? That’s where graph databases come into play.” She goes on to show how you may transform your relational database right into a dynamic graph database in Python.


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