Top 5 SQL challenges preventing you from mastering it - Feedback from 100+ Data professionals!
By Brian Laleye · July 19, 2022 · 12 min read
SQL is one of the most important data languages in the world.
It’s used by millions of people and it’s everywhere. It stands for Structured Query Language. It is the most common language used to interact with databases and by extension, this is the go-to language for data transformation.
All this makes SQL seem easy to use, right?
Unfortunately, using SQL is not as easy as it looks like. It’s a complex programming language and mastering it is not an easy task.
This is why I’ve contacted during several weeks more than 1,000 data professionals (Data Engineers, ETL & DBA experts, Data analysts and Data scientists) through Linkedin and asked them the following question: “Could you help me by sharing your 3 most important obstacles when using SQL?”
If you’re just getting started with SQL, then you’ll probably find that it takes some time to get used to the language and its nuances.
If you’ve been using SQL for a while, then I’m sure you’ve experienced these obstacles.
So, let’s dive into the main obstacles encountered while using classic SQL editors based on 100+ data enthusiasts’ answers!
Top 5 challenges shared by 100+ Data Professionals
There are many reasons why SQL is hard to master:
- It’s a declarative language, which means that it doesn’t contain procedural constructs like loops and conditionals.
- SQL is a relational language, which means that it has no data types or object orientation.
- The vocabulary of SQL is very large and diverse; you have to learn all about relational algebra, SQL standards, database management systems (DBMS) and other topics before you can even start writing queries. Especially, when it comes to cleaning and transforming data from different databases.
Let’s look at the main obstacles that make learning SQL so difficult.
SQL dialects and syntax: Learning SQL dialects & syntax is cumbersome
The first obstacle is the syntax.
It’s a shame that so many people have problems with SQL because it has a very simple syntax and it’s not that hard to learn.
In fact, you can learn it in less than a day if you have someone showing you the basic commands.
SQL has a very complex syntax because it was designed to be relational, which means you can join data from multiple tables in the same database.
But this also makes it hard to understand if you’re not familiar with it.
Interoperability of versions: Need to learn and adapt to each version
The second obstacle is the fact that there are many versions of SQL.
There are two main versions of SQL:
- ANSI SQL (the most common version)
- ISO SQL (the international standard)
Additionally, there are other variations that include MySQL, Microsoft Access, Oracle and PostgreSQL etc., which means that if you want to learn how to use these different tools, then there will be different syntaxes that you need to know about.
This makes mastering SQL even harder because you have to learn all these different variations at once!
If you are just starting out, then you will want to start with basic SQL queries and then move on to MySQL or PostgreSQL.
However, if you want to go beyond that and use other database engines such as Oracle or Microsoft SQL Server, then you will need more training and experience in order to make those work properly.
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Query speed and efficiency
The SQL language has been around for decades and is still a popular way to query data in a relational database.
But the language wasn’t designed for today’s big data applications.
SQL databases are optimized to work with predefined, fixed-length columns.
This makes it easy to write a query that retrieves the data you need, but it doesn’t support queries that require dynamic filtering or sorting.
While many database management systems have ways to optimize these queries, they are rarely as efficient as they could be because they don’t have information about how your data is structured or what operations you will perform on it.
Thus, the biggest problem with SQL is that it’s not very efficient.
If you’re trying to run a query across millions of rows, it can take forever to return results. That’s because each row needs to be read from disk and then processed by the CPU before returning the next row.
Consequently, many developers don’t know how to use it properly or efficiently because they just don’t have enough experience with SQL queries yet.
They might have spent some time working with databases but they still don’t know how to use them properly and this can lead to problems later on when trying to scale.
Understanding table structure and table relationships in SQL is hard
One of the most common mistakes that could be made when learning SQL is to jump right in and start writing queries without learning the basics first.
It’s like trying to ride a bike without ever having pedaled one before.
SQL is a declarative language, which means it doesn’t specify how to perform operations on data.
Instead, it specifies what operations should be performed and leaves it up to the database system to figure out how to do it.
This is great in theory because it allows databases to be portable across platforms, but in practice it means that SQL queries can become very complex because they need to take into account all possible ways that data can be related.
For example, if you’re trying to find all sales made by user A and then filter those sales by date range and product category, you’ll have to write a separate query for each way that user A’s sales data could be related (e.g., by date range or product category).
In contrast, a procedural language would allow you to write one query that says something like “Find all sales made by user A and then filter them by date range.”
This would work because procedural languages give you more control over how operations are performed.
Non-intuitive user interface
The rigid structure of SQL statements makes it difficult to write long scripts.
You have to break up long scripts into multiple statements, which can make them more complex than necessary and harder to read.
SQL doesn’t provide native support for functions or procedures, so you must use stored procedures written in other languages such as Java or C++ instead of using native SQL functions such as SUM() or AVG().
This can make your queries harder to maintain because they require changes throughout your application whenever there are updates made to the stored procedure codebase.
SQL doesn’t provide a way to pass parameters between stored procedures or database triggers (which run automatically after certain events occur), so developers often resort to using global variables, which makes their code harder to understand and maintain over time as well as increasing the risk of bugs due to shared state between different modules in an application.
Thus, in most cases, the interface consists of lots of text with no clear structure.
This can be really confusing for beginners and even for more experienced users who have never seen this type of interface before.
Another challenge: Sharing knowledge with teammates
Sharing your queries and insights with your teammates can be very beneficial.
It helps you to understand the problem from different perspectives and get business context, and it also increases the number of minds working on the same problem.
It should be possible to do the same thing with queries, but this is not the case.
Why is it challenging to share knowledge with teammates when using SQL queries?
Here are the main reasons:
- Queries are written individually not by teams
- Queries are not standardized
- Queries are not comparable
Another challenge is that people don’t have any easy way of comparing two different queries (like git diff does for code) since there is an infinite way to write a query in SQL.
This makes it difficult for team members who want to collaborate on projects or who want to learn from each other.
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