SQL for Data Visualization

4 min readMar 31, 2023

SQL is a powerful programming language that allows to store, manage, retrieve and manipulate data in relational databases. In simple words, it is a language, that allows users to communicate with the database using queries. SQL is employed in building complex software systems that support business operations and decision making. It has become an essential tool in managing data for many industries, such as, e-commerce, retail, finance, logistics, healthcare, etc.

It is important to note, that SQL is also a powerful tool for data visualization when integrated with other tools and programming languages like Tableau, Power BI, Excel, Google Data Studio and Python. They can build sophisticated visuals by retrieving data from a database and transforming it into a format that can be used by theses tools and languages.

General steps for data visualization using SQL:

1) Define Business Requirements:

Before you begin to build dashboards or reports, it is critical to define your business requirements. This allows you to focus your analysis on the most relevant data. And aids in building visuals that provides actionable insights to make informed decision for business growth. Business requirements can involve addressing specific objectives and problems that you want to solve, identifying relevant data, reporting frequency, audience for visualization, etc.

2) Data Source:

The initial step in building a visualization is to extract the necessary data from a database. In order to do this, we can write SQL queries to retrieve the required data from the database. The relevant data includes selecting necessary tables and columns, filtering data, and joining entities to get the big picture of the data. To do this, the following SQL commands such as SELECT, WHERE and, JOIN can be used.

E.g. SQL query to retrieve information about users purchase history (without aggregation)

Additionally, subqueries can be used to to filter data based on specific conditions. It allows users to write complex queries by nesting one query inside another. Also, views can be used to create virtual tables that combines data from multiple entities. A view always gives back the latest data. So, every time a user queries a view, the database recreates the view.

3) Data Extraction:

A) Export Data from SQL: The desired data in SQL can to exported into different formats such as excel, CSV, or JSON. Then, the compatible format can be used by the visual tools and programming languages for further data manipulation and visualization. For instance, you can use SQL to export sales data to CSV format and then import that data into Power BI to create interactive dashboard.

B) Import Data using Visualization Tools: Most visualization tools such as Power BI, Tableau and Google Data Studio support SQL integration. That is, they have built-in SQL integration, that allows the user to directly connect to a database, run queries and extract data.

C) Import Data using Programming Languages: Alternatively, programming languages such as Python or R use a SQL connector library to connect to the database. It allows you to run SQL queries and retrieve data in the format compatible with the visualization libraries. For example, libraries such as Matplotlib, Seaborn, Plotly or ggplot2 can be applied to create visualization.

4) Data Transformation:

After data extraction, the data may require to be converted into a useable format for visualization. This can involve, data quality check such as duplicates, missing value, null values, incorrect indexing, appropriate data types. Apart from it, calculating derived columns, filtering unnecessary columns and rows, and data aggregation can also be included. Example: Power BI and Excel allows you to launch Power Query which can perform the Extract, Transform, and Load(ETL) processing of data.

5) Data Visualization:

Once the data is in the appropriate format, it is ready to build visuals that can help in providing meaningful insights to drive business decisions. Visualizations tools provide various visual options such as, charts, graphs, tables, geospatial maps, and plots.

6) Build and Design Dashboard:

The next step is to create a story from the data by arranging the necessary visuals on the dashboard or report in a clear and concise manner. This involves selecting visuals, designing layout, adding labels, applying formatting. Also, can add slicers, filters, drill-downs, hover over information, clickable buttons and interactive elements that can allow users to explore the data in detail.

7) Publish Dashboard:

The final step is to deploy the dashboard. You can publish it on a web-based platform or export it to PDF or other file formats. This can be accessed by the users to understand the underlying trends or patterns in data.

Summary

Overall, SQL plays a critical role in building custom dashboards when used in collaboration with other tools and programming languages. By leveraging SQL queries, you can create meaningful visuals that can aid in making informed business decisions.