Mastering SQL: why SQL still matters and how to actually get good at it (1/10)

Why SQL still matters (and this will probably not change in the near future)

When searching for SQL on reddit what I most see about is that it's common place & easy to learn.

I actually agree with both these statements:

• SQL is easy to learn; but hard to master – even more so when you're working with it at scale with a tool like dbt or SQL mesh,
• It's the basis of Data work (I've worked with it at every company I've been)

Additionally and according to most articles I've seen online – SQL still goes strong and is listed as top 5 desirable data skillset.

Why SQL Still Matters in 2025: Trends and Career Insights

Let’s be honest—when you hear people talking about the “future of data,” you probably expect to hear about machine learning, artificial intelligence, and maybe even a little hype around no-code platforms. But here’s the twist: in 2025, one of the most in-demand skills in data is still the good old S

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Tools like dbt or SQL mesh further reinforce the need for SQL work – most transformation you'll do there will be in SQL.

Finally, SQL is the universal language of Data – everybody in the Data fields works / has worked with SQL.

While it's true that some Data work may require a more specific expertise (Spark, Distributed compute etc.), I believe that 90 to 95% of companies do not actually need this kind of complexity as the business needs business value from data – not unneeded complexity around Data work.

As such, I believe that one one the best thing you can do for your Data career is to become excellent at the basics, like SQL.

How should one become SQL fluent ?

The dual path to mastery

This is the tricky part, to get really good at SQL, you actually have to both;

• Write good (readable, maintainable, performant) code,
• AND have an understanding on how things operate behind the hood.

For instance:

• How does the query compiler behind your Data Warehouse works behind the scene ?
• What are indexes ?
• What are window functions ?
• What are the key differences of the files you're using to store your data ?
• etc.

Just as Zach Wilson puts it, there are effectively 5 levels to SQL:

More often than not, to get to level 5, you'd need years of SQL work in the Data trenches.

Don't despair !

I've decided to distill everything I know about writing good SQL in this series of 10 articles to get you to level 5 – fast.

They will be divided as such [*]:

• 1. "Why SQL still matters and how to actually get good at it", this article,
• 2. "The 20% of SQL that will power 80% your work",
• 3. "Aggregates & Window functions: a powerful tool at your disposal",
• 4. "Advanced SQL patterns: deduplication, pivoting, retention calculation etc.",
• 5. "Writing readable, maintainable SQL",
• 6. "Writing typed data with SQL & DML",
• 7. "Working with indexes, partitions and clusters",
• 8. "File formats: why are they importants & how to use them effectively",
• 9. "Writing performant SQL & working with the query profile",
• 10. "Other SQL tips & tricks"

[*] (might come back to update this division at a later date)

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