Raw data could be messy and inconsistent. Before any meaningful insights can be generated, the first thing we need to do is to clean and explore the data, which leads us to the important question: what tool should we use to facilitate this crucial step? Generally speaking, there are two popular languages to process data: SQL and Python, and although they are constantly brought up together, they are, in essence, quite different from each other.
SQL is an acronym for structured query language, it is a programming language designed to interact with databases. We can store, retrieve, modify data, and also manage databases with it. It has very simple and straightforward syntax yet is powerful and efficient when it comes to data filtering and aggregation. Together with databases, we can go through tens of millions of rows in a table in a relatively short amount of time. However, SQL does have its limitations; it doesn’t have many complex functions we can rely on for data manipulation, and there aren’t many libraries we can build on.
Python is an all-purpose programming language, you can use Python to build websites, applications, run scripts, and so much more. The reason it is so popular among data scientists is because of the extensive libraries it has that are tailored for data science, such as Numpy for numerical computation, Pandas for data manipulation, and Scikit-learn for machine learning. Due to Python’s all-purpose nature, and with the help of the libraries, we can perform complex data manipulation and transformation, it also supports data analysis and visualization.
Now that we have a general understanding of the two languages, when we are handed a large dataset, which should we use to process our data? I want to compare them from the following aspects:
- Functionality
- SQL: it has simple functions such as finding missing values, locating duplicate records, joining data tables, filtering by conditions, data aggregation, and basic calculation.
- Python: apart from the simple functions listed with SQL, it has many functions for complex data cleaning, such as standardization, normalization, and text manipulation. It also has many libraries we can call upon for all kinds of transformations we might want to perform on our data. There are also libraries that facilitate data exploration.
- Performance
- SQL: once the data is successfully loaded into the database, we can access it whenever we want, there is no extra loading step, we simply need to connect to the database and give our SQL query. And because of the design of databases, SQL performs very well for simple queries such as filtering and grouping, even when facing big tables.
- Python: every time we open up our program, we have to load the data into our runtime environment, which would typically be stored in the runtime environment’s RAM, which means the performance of the program highly depends on our computer’s specifications. In the face of large amounts of data, the loading process can easily cram our computer and lead to overall low performance. However, since it is stored in RAM, it allows easy access from the program. If the computer has enough RAM space to support the functionality of other core processes, we may still see a decent performance speed.
- Flexibility
- SQL: each database has its own SQL dialect, it’s focused on communication with the target database and target tables. Procedures written for a certain purpose can not be easily adapted to other tables, and there’s minimal integration with other systems.
- Python: highly flexible and adaptive, can be used in different scenarios, and can be integrated with different systems or languages using APIs or libraries.
In conclusion, both languages have their unique advantages. As aspiring data scientists, we definitely should have both skills under our belts. Generally speaking, when we have a large dataset, we should utilize the database and do a round of basic data cleaning first, possibly breaking the data into smaller segments for manipulation, then, we can employ Python to perform more complex data transformation for more precise analysis.
One more thing I need to mention about the database is that it is a better and more structured way of storing data, it can provide backup and redundancy compared to file storage, and it’s absolutely essential if you have multiple users. Regardless of the data size and your preferred way of data preprocessing, I think it’s always good practice to save your precious data in a database.
Columnist: Ricki Tsao