Series: SQL For Spreadsheet Users — Part 1
How To Excel in SQL With Your Spreadsheet Skills
Introduction
Undoubtedly, the spreadsheet has been one of the most widely used computer software applications since the invention of the computer. Spreadsheets have revolutionised how we perform data analysis, data mining, and gain insights from data. They enable us to delve deeper into data, facilitating unprecedented decision-making.
While there are numerous spreadsheet software options in the market, Microsoft Excel, Google Sheets, and LibreOffice collectively dominate the majority of the market share. Although these systems have played pivotal roles in data analysis, data mining, and informed decision-making, they are no longer sufficient given the volume of data we analyse daily and the increasing complexity data analysts face in making informed decisions.
In over 17 years of experience working in various data related roles, including data analyst, data engineer, data architect, business analyst, and data warehouse manager. I have extensively used spreadsheets and Structured Query Language (SQL) to extract data, analyse data, and make informed decisions for thousands of hours, and helped other spreadsheet users to acquire SQL skills using their existing spreadsheet knowledge.
I’ve observed that while spreadsheets like MS Excel are suitable for small datasets (around 1,000 rows), the reality is that many datasets today have thousands, if not hundreds of thousands, of rows. These datasets require pivoting, analysis, mining, and extraction of insights from different corners.
The challenge arises when dealing with spreadsheets containing thousands of rows; they significantly slow down your data analysis and make it painstakingly difficult to analyse complex sets of related data. It takes about 10,000 rows on a 16GB RAM and 3GHz computer to experience lagging if not crashing in a spreadsheet. This may require a level of patience you simply don’t have.
This series is for you if you’ve been working with data using spreadsheets and want to enhance your practical skills, becoming comfortable working with data using SQL. It’s also for those who wish to learn quickly and apply these skills to their advantage, standing out as a data analyst equipped with practical SQL skills and real-life examples.
Throughout this series, you will learn how to retrieve information using SQL, create links, and derive meaningful insights from linked data in a real database. By the end of this series, you will feel confident working with any SQL database, including MySQL, PgSQL, MSSQL, Oracle database, or SQLite.
