Why You Should Keep SQL at the Center of Your Analytics Workflow

Over the years, many tools have emerged that abstract away SQL-like analytics tasks and move them outside of the database. Some of the most common include:

• Tidyverse – R

• Pandas – Python

• Polars – Python

• Excel

• Tableau

• Power BI

• KNIME

These tools are incredibly useful—I especially love Python and R!

But here’s a word of caution:

The farther you move away from SQL and push your data transformations outside the database, the more likely you are to encounter challenges such as:

• Vendor lock-in

• Slower performance

• Less accurate or inconsistent reporting

• Longer analytics development cycles

• Difficulties in auditing and troubleshooting

A Better Mindset: SQL-First

What I recommend is adopting a SQL-first mindset—do as much work as possible in the database, then use external tools for visualization, modeling, or niche tasks.

For example:

• In Tableau dashboards, I recommend building each component from a dedicated SQL view. All views should pull from a central, regularly refreshed detail table in your database.

• In Python, if I need to run a machine learning model using scikit-learn, I pull only the necessary data from a SQL view—just the features and records I need. I don’t query SELECT * FROM table into multiple DataFrames, join them in RAM, and aggregate them locally. Even though Polars makes this kind of in-memory processing much faster than Pandas today, it’s still better to offload joins and aggregations to the database.

Final Thoughts

SQL isn't just a legacy skill—it's the foundation of reliable, scalable, and maintainable data systems. Treat your database as the engine, and let everything else be the interface.

Thanks for reading.