5 Reasons Why SQL is Important for Data Science
Structured Query Language (SQL) is a powerful language that is essential for data science because it can perform complex queries for specific datasets in a database.
As it is so commonly used in data science, learning what SQL is and why it’s important is of paramount importance.
Large tech companies and startups use SQL, including Uber, Netflix, Airbnb, and Illumina.
As such, I will be sharing and discussing 5 reasons why SQL is important for data science and give you some examples of how they can be used.
Read on to find out more!
What is SQL?
Standard Query Language (SQL) is a standard computer language for relational database management and data manipulation. It is used to Create, Read, Update, and Delete (CRUD) data in databases. In addition, SQL can be used to create views, indexes, and stored procedures in databases.
SQL is considered programming because it is Turing-complete and can perform computer tasks.
Many find learning how to code in SQL easy and takes a short amount of time to learn because of its human-readable syntax.
What are the Reasons Why SQL is Important for Data Science?
1. SQL Allows You to Query and Analyze Data
One of the most important reasons why SQL is important for data science is that it allows you to query and analyze data stored in databases.
This is essential for data science because oftentimes, the data that we need to analyze is too large or complex to be processed by traditional means such as Excel.
With SQL, we can easily select the specific data that we want to analyze and perform various operations on it such as aggregation, filtering, and sorting.
This allows us to quickly and efficiently gain insights from our data without having to spend a lot of time pre-processing it.
Okay, here’s an example of what a SQL query can look like.
WHERE sales_date BETWEEN ‘2022-01-01’ AND ‘2022-12-31’
ORDER BY sales_amount
In the example above, we are querying the sales_data table for all records where the date of the sale is between 2022-01-01 and 2022-12-31.
We are then ordering the results by sales_amount and limiting the number of records returned to 100.
This query would be useful for data science if we wanted to analyze the sales data for a specific period of time or if we wanted to find the top 100 selling products.
This also allows us to automate the analysis to be performed over and over again in a pipeline. In Excel, these actions will need to be performed manually.
2. SQL is a Powerful Tool for Data Mining
Another reason why SQL is important for data science is that it’s a powerful tool for data mining.
Data mining is the process of extracting valuable information or insights from large data sets.
With SQL, we can easily select and retrieve specific data from databases which can then be used for further analysis.
This allows us to quickly gain insights from our data without having to spend a lot of time pre-processing it.
By utilizing SQL queries, you’ll be able to query from a relational database management system (RDBMS) directly at a large scale without importing into another program and writing programming language code (R/Python).
Many times, importing large datasets into R or Python can be really computationally demanding, so by querying the data in SQL into a smaller dataset, it can be fed into your data science programming IDE of choice and analyzed.
Typically, in Python, subsequent data science packages such as sci-kit-learn and the caret package in R can be used to perform machine learning techniques.
For example, let’s say we want to find out the average age of our customers. We can easily write a SQL query to select all of the customer records and calculate the average age like so:
This query would return the average age of all customers in the customer_data table.
We can then use this information to segment our customers by age or target them with specific marketing campaigns.
3. SQL Connects Data Sources to Data Warehouses
Another reason why SQL is important for data science is that it’s used to connect data sources to your data warehouse.
A data warehouse is a central repository where all of your organization’s data is stored. Examples of cloud-based data warehouses include Amazon Redshift, BigQuery, and Snowflake.
The data stored can be from all aspects of a business such as customer data, sales data, financial data, etc.
The data warehouse then serves as the single source of truth for all data analytics and reporting.
In order to populate a data warehouse, data needs to be extracted from various data sources such as databases, CSV files, JSON files, etc.
This is where SQL comes in. SQL can be used to query the data from these various data sources and load it into the data warehouse.
Once the data is in the data warehouse, it can then be used for further analysis or reporting.
Traditionally, this process is known as Extract, Transform, Load (ETL). However, in a modern data stack, however, the paradigm is shifting to Extract, Load, Transform (ELT).
There’s actually a difference between ETL and ELT!
In ELT, the data is extracted using SQL and then loaded into a cloud data warehouse where it can be transformed by data transformation software such as dbt.
This process of data integration from various sources such as Adobe Analytics or Facebook ads is something that Rivery is great at!
SQL is an essential part of ELT because it’s used to extract data from various data sources and load it into the data warehouse.
The advantage of ELT is that it allows for more flexibility and creativity in the transformation process because all of the data is stored in the data warehouse.
Data scientists, data analysts, and data engineers can then share a single source of truth in the data warehouse. This allows the entire data repository to be synchronized and coordinated across an organization.
This means that we can try out different data transformation techniques without having to worry about breaking anything.
It also makes it easier to track changes and audit the data transformation process.
As the data is clean because of the transformation process, data scientists can spend less time on data preparation and focus more on data analysis which is the most important part of their job.
4. SQL is Used to Clean and Prepare Data for Analysis
Another reason why SQL is important for data science is that it’s used to clean and prepare data for analysis.
We’ve all been there before. We’ve downloaded a dataset only to find out that it’s full of missing values, incorrect data, and other issues.
This can be really frustrating because it can take a lot of time to clean and prepare data manually.
Fortunately, SQL can be used to automate this process.
SQL can be used to select specific columns, rows, or cells that meet certain criteria.
For example, let’s say we want to select all of the customer records where the age is greater than 30. We can easily write a SQL query to do this:
WHERE age > 30;
This query would return all of the customers’ records where the age is greater than 30.
We can then use this data for further analysis or reporting.
SQL can also be used to update incorrect data.
For example, let’s say we want to update all of the customer records where the age is greater than 30. We can easily write a SQL query to do this:
SET age = 31
WHERE age > 30;
This query would update all of the customers’ records where the age is greater than 30 and set the age to 31.
Before you run a SQL query that updates data, do make sure that you have a backup as it will replace the data in a database.
SQL can also be used to delete incorrect data.
For example, let’s say we want to delete all of the customer records where the age is greater than 30. We can easily write a SQL query to do this:
DELETE FROM customer_data
WHERE age > 30;
This query would delete all of the customers’ records where the age is greater than 30.
However, I will caution against using the DELETE function as data removed from the database cannot be recovered.
SQL can be used to do all of this and more. It’s a powerful tool that every data scientist should know how to use.
5. SQL is a Key Component of Big Data Infrastructure
SQL is also a key component of big data infrastructure and platforms.
For example, Apache Hadoop is a big data platform that uses SQL for processing and querying data.
Hive, Impala, and Presto are also big data platforms that use SQL for processing and querying data.
Spark is a big data platform that uses SQL for processing and querying data.
As you can see, SQL is a vital part of big data infrastructure and platforms.
Without SQL, these platforms would not be able to function. In fact, it’s commonly taught in AI courses because it’s such a staple among data scientists.
This is why it’s so important for data scientists to know how to use SQL.
SQL is an important part of DataOps and knowing how to use SQL is an important skill for data science.
It’s used to clean and prepare data for analysis, it’s a key component of big data infrastructure and platforms, and it makes it easier to track changes and audit the data transformation process.
As data science is growing in importance by the day, make sure you tap into incorporating SQL into your organization.
If you’re a data scientist and not already familiar with SQL, then I suggest you start learning how to code in it as soon as possible. It will make your life as a data scientist much easier because datasets don’t have to be loaded and cleaned in your Python script anymore!
If you’re a business owner or part of key management, make sure to hire talents who know how to utilize SQL as this can have huge repercussions for building up data science assets in your organization.
I hope you’ve learned more about SQL for data science and thanks for reading!