SQL for data analyst or how to get the insights from the data base

24 June 2024

If you ever googled ‘SQL for data analyst’ or you would like to know how to get the insights from the data base, this article is for you.

Today, I’ll talk about the key elements of SQL for analysts and show you how to effectively extract data from a database. The topic might seem complex, but I promise to explain everything in a simple and accessible way.

Why is SQL Important for Data Analysts?

SQL (Structured Query Language) is the primary tool for any data analyst. If you dream of a career in this field, you need to master SQL at a good level. Why? Because most of the data you’ll work with is stored in databases, and SQL is the key to extracting and analyzing it.

Reading SQL of Others

One of the first surprises for beginner analysts is that a lot of time is spent reading SQL written by others. In a company, you’re usually not the only analyst, which means you need to understand and interpret SQL code written by your colleagues.

Why is this important? Firstly, it facilitates teamwork. Secondly, by reading others’ code, you learn new techniques and ways to solve problems. Remember to write your queries in a clear and understandable manner – it will make life easier for others (and for yourself!).

Writing SELECT Statements

The most important part of a data analyst’s job is writing SELECT queries. These commands allow us to select the data we are interested in from the database. If you’re starting your journey with SQL, focus on learning the various aspects of the SELECT statement:

  • Joins – combining tables,
  • Subqueries – nested queries,
  • Conditions in WHERE – filtering data,
  • Window Functions – advanced calculations across a set of table rows.

Remember that 80-90% of your work will be related to writing SELECT statements, so it’s worth dedicating a lot of attention to this topic.

Optimizing SQL Queries

Query optimization is another crucial aspect of working with SQL. At first, it may seem like your queries run quickly, but as you start working with larger databases, the differences in execution time can be significant.

What should you keep in mind? Test your queries using:

  • Indexes – speed up data retrieval,
  • Partitioning – dividing large tables into smaller parts,
  • Explain Plan – shows how the database executes queries.

Optimization is a balance between code readability and performance. Try to find a golden mean!

Checking Data Quality

As an analyst, you need to be sure that the data you work with is of high quality. How to do it? Regularly check for:

  • Duplicates – may indicate errors in the data,
  • Nulls – missing values can affect analysis results,
  • Timestamps – ensure that dates and times of operations are logical and correct.

Checking data quality is an integral part of an analyst’s job. It helps avoid erroneous conclusions and ensures that your analyses are reliable.


Working with SQL is fundamental in a data analyst’s life. Reading others’ code, writing effective SELECT statements, optimizing queries, and checking data quality are key elements you need to master. Remember, SQL is not just a tool but an art – the more you practice, the better results you’ll achieve.

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