Python vs SQL for Data Analysis: comparing performance, functionality and dev XP
The clean division of data analysis labor between Python and SQL seems to be fading with tools like dbt, Snowpark and dask-sql. The article shared below compares the two languages in terms of performance, functionality and developer XP. 

Quick summary:

**Performance**  
Running SQL code on data warehouses is generally faster than Python for querying data and doing basic aggregations. This is because SQL queries move code to data instead of data to code. That said, parallel computing solutions like Dask and others that scale Python code to larger-than-memory datasets can significantly lower processing times compared to traditional libraries like pandas.

**Functionality**  
SQL’s greatest strength is also its weakness: simplicity. For example, writing SQL code to perform iterative exploratory data analysis, data science or machine learning tasks can quickly get lengthy and hard to read. Python lets you write free-form experimental data analysis code and complex mathematical and/or ML code. The absence of a vibrant and reliable third-party library community for SQL is also a problem compared to Python.

**Developer XP**  
Python makes debugging and unit-testing a lot easier and more reliable. While dbt has added code versioning by forcing the use of Git, SQL diffs are still harder to read and manipulate than diffs in Python IMO.

**Conclusion**  
While it's tempting to frame the debate between SQL and Python as a stand-off, the two languages in fact excel at different parts of the data-processing pipeline. One potential rule of thumb to take from this is to **use SQL for simple queries that need to run fast** on a data warehouse, **dbt for organizing more complex SQL models**, and **Python with distributed computing libraries like Dask for free-form exploratory analysis and machine learning code** and/or code that needs to be reliably unit tested.   


Full article:  
[https://airbyte.com/blog/sql-vs-python-data-analysis](https://airbyte.com/blog/sql-vs-python-data-analysis)