Introduction
SQL is regarded as the most important technology for a Data Analyst to learn. It’s even said that one can’t refer to oneself as a data analyst if one doesn’t know SQL! Why is this language so important for data Analyst and BI professionals?
About SQL
As it is known, SQL stands for structured query language, and it is the language used to communicate with relational databases (RDBMS: Relational Database Management System). SQL is used in many ways by different professional that deal with data such as Data Analysts, Data Scientists, Data Engineers, ML Engineers, Backend developers, and Database Admins.
About the Data Analysis job
Data analyst are responsible for collecting, retrieving, analyzing, and extracting insights from data, then report the insights to stakeholders. The stakeholders would eventually make data-driven decisions using those insights.
From where does the data that the analyst uses come from? It can come from different sources such as spreadsheets like MS Excel or Google Sheet, Web Scraping or APIs if the data analyst is familiar with a programming language, and most importantly, from a Database (more precisely, a data warehouse)
Databases are vital in the world of modern Data Analytics. They serve as a centralized location for all the company’s data, because they are performant at a very large scale. In the case of a Cloud Data Warehouse like Google’s BigQuery solution, the data warehouse can store Terabytes of data and still query them in a performant way.
For reference, BigQuery can store and process hundreds of billions of rows, while Microsoft Excel can store around one million rows of data at most.
In order for a data analyst to take advantage of all the benefits that comes with databases, one must learn the only language that they understand, SQL.
SQL in the world of Data Analysis
Now that we understood SQL, and the job of a Data Analyst, let’s see how they come together.
The better an analyst is at SQL, the better he/she is at writing queries that does exactly what you want, and thus the better he/she is at answering the questions that would solve the problem.
Nowadays, Enterprises store all the business related data in one central location, often, it is a Data Warehouse. Data Warehouse are relation system that use SQL to basically do anything, and data analysts retrieve the required information from the warehouses using SQL’s SELECT statements.
Answering questions immediately:
The ability to answer business related questions immediately by getting the data when needed and not relying on others to provide it. This level of freedom is a noticeable factor in both the productivity and the creativity of the data analyst and BI professional.
Retrieve grained data:
SQL provide the ability to retrieve exactly what you want from the database. An analyst can apply filters to the data, aggregation functions, Grouping, sorting, joining tables, and much more. This level of flexibility gives the analyst the ability to process at the most granular level, and that plays a major role in the final output, the report.
One language for all systems:
The ability to access data stored in any Relational Database System, and since analytics is usually built upon such systems, the analyst will hit the ground running even if the company decides to change the warehouse used.
The system integration:
The ability to work in a well integrated system, for example: Storing data on BigQuery, visualizing and reporting the output on Looker (Both these two are Google Cloud Platform products and work seamlessly together). Each cloud provider for example has their own integrated solutions.
I this above example, Looker uses SQL to fetch data from BigQuery, and display it on its Dashboards.
Conclusion
SQL is mandatory for data analysts, and the importance of mastering it makes the difference between juniors and experts.
Check out why every Data Engineers must learn SQL!
