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Learn Data Science with me & Please support my work

As a data scientist, you probably use SQL on a regular basis to query, manipulate, and analyze data. SQL is a powerful and versatile language that can handle a variety of tasks, from simple filtering and aggregation to complex joins and subqueries.

But are you using SQL to its full potential? Do you know how to write efficient, elegant, and expressive SQL queries that can solve challenging data problems?

In this article, we will explore 10 advanced SQL techniques that can help you level up your data science skills. These techniques are not only useful for data analysis, but also for data engineering, data modeling, and data visualization.

By learning these techniques, you will be able to write better SQL queries, optimize your code performance, and impress your colleagues and clients with your data insights.

<aside> 💡 Here are the 10 advanced SQL techniques we will cover:

  1. Window functions
  2. Common table expressions
  3. Recursive queries
  4. Pivot and unpivot
  5. Lateral join
  6. JSON and XML functions
  7. Full-text search
  8. Temporal tables
  9. Spatial data and functions
  10. Machine learning with SQL </aside>

Let’s dive in!

1. Window functions

Window functions are one of the most powerful features of SQL. They allow you to perform calculations over a set of rows that are related to the current row, without having to group or aggregate the data. Window functions can be used for ranking, partitioning, calculating running totals, moving averages, percentiles, and more.

The syntax of a window function is:

<window_function> OVER (
    PARTITION BY <expression>
    ORDER BY <expression>
    [frame_clause]
)