3 SQL Features That Ensure Deep Data Analysis For Enterprises

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2 years ago

Data analysis is integral to smooth business functioning these days. SQL is the language of choice if you want to power data analysis. First developed by IBM in the 1970s, SQL definition states that it’s a structured query language. However, it’s far more powerful than that and has come a long way from its humble origins to establish itself as the go-to choice of data analysts everywhere.

As data analytics are becoming democratized in organizations, business users have faced the task of coming to grips with SQL’s demands. While the language is simple on the surface and boasts an intuitive command structure, SQL can help you execute complex data analysis easily.

In this article, you’ll learn about 3 SQL features that simplify data analysis and give you the insights you need to make a difference.


Joins are a basic functionality within SQL. A join, as the name suggests, combines two different tables and helps you display data that is contained in both. For example, if you have customer data in table A and account data in table B, you can join A and B using a common key (usually a shared column between tables A and B) to display data.

There are 4 types of joins within SQL. These are inner, full, right, and left joins. The FROM clause in the SQL command structure helps you define which type of join you would like to execute. Inner joins return records that are common to both tables.

A left join returns records from the first table mentioned in the FROM clause and matching records from the second table. Similarly, the right join returns all records from the second table while returning matching records from the first. Full joins return all records from both tables when there’s a match between any column in both tables.

Joins are facilitated using columns called primary keys. Primary keys, usually an integer value, are common records between the tables being joined. For example, a customer data table and accounts table can share a column called customer ID.

This primary key helps you join customer records to account information easily.

Aggregate functions

While joining data helps you create reports that combine data from multiple tables, it doesn’t help you group or order data in a helpful fashion. A simple join clause gives you a data dump that can be tough to parse.

SQL helps you organize data using helpful functions called group by and order by. While these functions sound the same, they lead to different results. In addition to these clauses, you can also use the having clause.

For instance, you can display data “having” a certain character present in the results. This helps you filter data and display only those results that are relevant to your analysis. Having clauses are pretty intuitive to use and will simplify data analysis significantly.

Order by clauses are also pretty intuitive. Let’s say you want to view the data within the customer table listed alphabetically depending on the customer’s last name. Specifying the order by clause after the from clause will give you results that are ordered by the last name.

You can also list numerical results in ascending or descending order. The order by clause lists results in ascending order by default. For example, let’s say you want to view customers ordered by their home state. Using the order by clause on the customer state column will list customers according to the state they live in.

Customers living in states beginning with “A” will be listed first, followed by “B”, and so on. However, what if you want to count the number of customers in states that begin with the letter “A”? This is where group by comes in handy.

While order by lists results in ascending or descending order, group by helps you execute aggregate functions such as sum, count, and average on data. You can thus group your result by state and then calculate the count of users in each state. Your final results will display the state name and the count of users in each state, along with any other data you want to view.

Data organization

How often have you been tasked with compiling a report first thing in the morning? If the data that goes into the report is spread out across your database, you’ll have to write a complex query every day to gather and organize it. What if you could create a simple dashboard and send it to recipients?

SQL helps you do this by creating views. A view is a collection of data that you can easily distribute. Best of all, you can restrict user access to views, ensuring only the right users view data in a view. Views are temporary tables and have functionality limits.

However, they’re a great way of organizing your data in a simple interface and distributing the results for further insights. You’ll also save time by avoiding writing basic queries over and over.

Simple and powerful

SQL is popular amongst data analysts for a very good reason. It’s simple to use and is intuitive. However, its simplicity belies how powerful it is for data analysis. The SQL features highlighted in this article will help you extract the most from your data.

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