Python vs SQL: Which is Better for Data Analytics?
The increase of data in the world means a need for better and more efficient ways to handle and analyze it. Programming languages are a great way to analyze data. There are two popular programming languages used for data analytics – Python vs SQL. These programming languages provide data analysts with an effective tool to manage, analyze, and process large amounts of data.
In this article, we’ll take a look at SQL and Python and find out which one is better suited for data analytics.
What Is SQL?
Structured Query Language (SQL) is a programming language designed for managing data within a database. Many industry professionals use SQL to manipulate, maintain, and access data stored in a database system. SQL uses different statements and clauses to extract information from databases.
Data on webpages, applications, and enterprise software packages that are stored in relational databases can be extracted using SQL queries. These queries are used to search through large amounts of data, allowing data analysts to create reports and other documents based on the results.
What Is Python?
Python is a general-purpose scripting language used to develop programs, especially those that perform computations or do data analysis. The popularity of Python has grown over the years, and data analysts can use Python to look through large amounts of data and extract useful information from it.
A key characteristic of Python is that it’s versatile. It allows programmers to create scripts that are easy to read and understand. This makes it an ideal programming language for data analysts who need to quickly process large amounts of data. With Python’s simple syntax, data analysts don’t need to spend much time writing complex coding.
Is One Better Than the Other?
SQL and Python have different advantages for analyzing big data sets. While both these programming languages are suitable for data analytics, they each have their strengths and weaknesses. Let’s look at what makes each programming language better than the other.
Why Use SQL?
SQL is a powerful programming language that allows users to query, modify, and analyze data from the database. Data analysts may prefer to use SQL over Python because it allows them to focus on accessing and extracting data. SQL uses commands and functions for data analysis, making it easier for data analysts to manipulate data from the database.
However, since SQL is an older programming language, it lacks some modern features found in Python. SQL has limitations when working with large datasets, but it’s known for its ability to combine multiple tables efficiently.
Why Use Python?
Python is one of the most popular programming languages used by developers. Data analysts may want to use Python to create powerful data-driven applications because this scripting language can easily be integrated into existing systems. If SQL uses commands and functions, Python utilizes programming libraries to perform calculations and solve problems. It provides great support for data analysts who want to conduct advanced research.
In addition, Python has more applications outside of data analysis. This means that if a data analyst learns Python, then they can apply their skills in other areas as well, such as web or app development.
What Programming Language Should I Learn?
At the end of the day, the programming language you choose for data analytics will depend on your specific needs. While SQL and Python overlap in functionality and usage, they each have their unique benefits. The best way to choose which programming language to use for data analytics is to identify what kind of data you’re working with.
For instance, data analysts who need to retrieve information from a database may be more comfortable with SQL. SQL provides an easy-to-use interface that quickly connects to databases and retrieves data.
However, if your data requires further manipulation before it’s ready for analysis, you might find yourself better off using Python. Python provides a lot of flexibility in manipulating data since it’s more powerful and can handle larger amounts of data. Any computations or experiments with the data can easily be done in Python instead.
No matter what kind of programming language you decide to use, it’s still a good idea to learn the basics of SQL and Python. Whether you’re doing data analytics for marketing, finance, or another industry, understanding how these languages work will help you get up to speed faster. Data analysts will also be more aware of the programming language’s features and how they apply to different situations.
The Bottom Line
Data analytics is crucial for businesses and organizations that handle massive amounts of data. As a result, programming languages like SQL and Python are essential tools for data analysts everywhere. If you’re choosing between SQL and Python to analyze data, you have to know that both languages are suitable for certain types of data analysis. By choosing the right language, you can optimize your workflow and increase your productivity.