The Myth of “One SQL to Rule Them All”
When I first started out as an analyst, I thought SQL was SQL. Like English is English, right? You might get a different accent in Texas or Toronto, but you can still order coffee with few issues.
Then I opened PostgreSQL after months in SQL Server and wrote:
SELECT TOP 10 * FROM my_table;
If you’ve used Postgres before, you know my mistake. Postgres doesn’t recognize TOP. It uses LIMIT, like every other “normal” dialect. (SQL Server is on its own planet, I swear…)
And that’s okay, that’s how we learn. Make mistakes, break things, see what happens. SQL works like that, too.
But that leads to a big problem a lot of data analysts and data scientists have to deal with. You learn SQL once, switch companies or tools, and suddenly the code you’ve always used doesn’t run. I used BigQuery in my previous role, and now I use SQL Server, and I had to pick up a few new tricks to get my queries to work. They are not big differences, mind you, but tiny discrepancies that get annoying to have to constantly google.
That’s why I made my SQL Dialect Cheat Sheet. I actually made this cheat sheet for myself for work, but I knew a lot of others would appreciate something similar, so I spruced it up and made it much more presentable. I haven’t come across anything like it before, which is why I created it, so grab yours if you’re tired of googling the same syntax differences every week:
What Do We Mean by “SQL Dialects”?
Think of SQL like English, and like in English, there are different ways of saying the same thing.
Here’s a simple example:
- In the UK, they say “lift.”
- In the US, it’s “elevator.”
- Here in Canada, we might use either, though it’s typically “elevator.”
Same meaning, different word.
Basically, if you know the concept, you can adapt quickly and easily, but if you insist on using “elevator” in London, you’ll get some puzzled looks.
SQL works the same way. The concept (like “get the top rows,” “add five days to a date,” “replace a null”) is universal. But the expression changes depending on the database.
SQL is the umbrella language, and under it we have dialects like SQL Server, PostgreSQL, BigQuery, and many more.
Why Do Different Dialects Even Exist?
SQL (Structured Query Language) was standardized in the 1970s, but vendors started adding their own “extensions” to differentiate themselves, which are referred to as dialects or flavors. Over time, these extensions stuck. That’s why:
- SQL Server uses TOP 10
- PostgreSQL uses LIMIT 10
- and Oracle uses ROWNUM
But it doesn’t end there.
The core SQL keywords (SELECT, WHERE, JOIN, GROUP BY) remain consistent. But once you start getting into dates, strings, temporary tables, or advanced analytics functions, the nuances show up.
In other words, they’re all ordering coffee in English, but one says “to-go,” another says “takeaway,” and a third says “for pickup.” Those poor baristas…
The Big Players in the SQL World (and Their Flavors)
- SQL Server: Microsoft’s enterprise database that uses T-SQL syntax.
- PostgreSQL (aka. ‘Postgres’): Open-source, standards-friendly, and flexible. A lot of modern tools borrow from it.
- MySQL: Hugely popular in web apps, especially for startups.
- Oracle: Old, powerful, and infamous for doing things its own way.
- BigQuery: Google’s cloud data warehouse. Fast, scalable, but picky about syntax.
- Snowflake: Cloud-native, warehouse-first, often feels like SQL Server but with quirks.
Others exist too: SQLite (lightweight, used in apps), Redshift (Amazon’s PostgreSQL-based warehouse), and even more niche systems.
Where the Dialects Really Differ
1. Date and Time Handling
Dates and times show most of their differences across dialects, and they are the most commonly searched syntaxes (at least in my search bar). This was one of the main reasons I needed to create my cheat sheet.
For example, a simple thing like adding five days to a date looks different almost everywhere:
SQL Server:
DATEADD(DAY, 5, my_date)
PostgreSQL:
my_date + INTERVAL '5 days'
MySQL:
DATE_ADD(my_date, INTERVAL 5 DAY)
It’s the same calculation when you break it down, but every database insists on its own way of writing it. It’s a pain!
Tired of googling the same SQL problems every week?
Learn and practice SQL here:
2. Limiting Rows
SQL Server:
TOP
PostgreSQL/MySQL:
LIMIT
Oracle:
ROWNUM or the newer FETCH FIRST N ROWS ONLY
BigQuery/Snowflake:
LIMIT
This is usually the first thing new analysts (including myself) get wrong when switching platforms.
Tired of googling the same SQL problems every week?
Learn and practice SQL here:
3. String Functions
Even simple operations like combining two strings change. SQL Server uses +, PostgreSQL uses ||, and most others prefer CONCAT(). And if you throw in NULL values, things behave differently depending on the system.
DataCamp Practice SQL and Python in your browser >> 4. NULL Handling
This is a subtle but important one. SQL Server uses ISNULL(), and almost all the rest use COALESCE(). MySQL adds IFNULL(). If you’ve ever had a report suddenly display blanks instead of zeroes, you know how much this matters.
5. Temporary Tables
SQL Server lets you quickly create new temp tables with a # prefix:
SELECT * INTO #temp_table FROM my_table;
PostgreSQL (and others) require a more formal CREATE TEMP TABLE …. Snowflake and BigQuery handle temp objects differently again, with stricter session rules.
6. Window Functions
Thankfully, window functions (ROW_NUMBER(), RANK(), SUM() OVER (…)) are more standardized, but there are still a few quirks (because, of course, why wouldn’t there be?). For example, BigQuery sometimes requires an explicit ORDER BY even when other systems don’t.
All I know is that these differences are enough to make your head explode!
Why This Matters for People Who Work With Data
You’re probably wondering, “Who cares?” I know, I wish we didn’t have to worry about these syntax differences. Mind you, we don’t need to memorize them all, because they are easy to pick up and look up when needed. The core/basic SQL syntax is what matters.
If you’re working in one database for years, you can get away with learning only its flavor. But in reality, you’ll probably work in a new role or company a few times in your career, and you’ll notice a few things:
- Many companies run multiple systems at once.
- Switching jobs often means switching SQL dialects.
- Cloud warehouses (BigQuery, Snowflake, Redshift) are growing fast, and they each add their own little spin on things.
Knowing where these differences are will save you hours of debugging. More importantly, it makes you adaptable, and that speaks volumes when applying for new roles.
Tips for Navigating Different Dialects
- Start with the core SQL keywords:
- SELECT, JOIN, WHERE, and GROUP BY are universal.
- Look up functions before copying code:
- Don’t assume something that works in MySQL will work in another platform. And don’t copy-paste blindly; make sure you understand what’s going on and why.
- Keep a reference handy:
- Whether it’s my cheat sheet or your own notes, side-by-side examples will help you, immensely.
- Learn the defaults.
- SQL Server defaults to Sunday as day 1 of the week. PostgreSQL starts at 0. These small things matter in date calculations.
- Think in concepts, not in code:
- If you know why you’re writing a query, it’s easier to adapt it to new syntax. Try to translate what you’re querying into plain English, using analogies like I did earlier. For example, if you know you need to “add 5 days,” you’ll figure out the syntax more easily.
Final Tips
SQL isn’t just one language; it’s a family of languages. Just like you wouldn’t expect British, American, and Australian slang to be identical, you shouldn’t expect SQL dialects to match word-for-word.
The good news is that once you learn the differences, you’ll stop panicking when your query fails. If you know the general functions you need, then all it takes is a few tweaks on how you write them.
I built my SQL Dialect Cheat Sheet as a quick reference because I’ve been in that position too many times. If you’ve ever spent 20 minutes wondering why TOP 10 doesn’t work, you’ll get the value of having all the dialects lined up on a sheet for easy look-up.
And as a thank you for reading this post, you can get 30% off the cheat sheet by using code SQLDIALECTBLOG30 at checkout.
Grab yours here:
Frequently Asked Questions About SQL Dialects
Q1: Why are there different SQL dialects?
SQL was standardized decades ago, but database vendors like Microsoft, Oracle, and PostgreSQL extended it with their own features. Over time, those variations became permanent, creating dialects.
Q2: Which SQL dialect is most “universal”?
PostgreSQL is often closest to the SQL standard, but no dialect is truly universal. Core commands like SELECT, JOIN, and WHERE work everywhere, but functions and date handling differ.
Q3: Do I need to learn every dialect?
Not in full. Focus on one main dialect (like SQL Server or PostgreSQL), then learn the key syntax differences in others. A reference chart or cheat sheet helps you translate quickly.
Q4: Is SQL going away with Python and modern tools?
Not at all. SQL remains the foundation of data analytics and engineering. Even tools built on top of databases still rely on SQL under the hood.
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