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SQL is a language used to work with data stored in relational databases. It allows you to ask questions of a database, retrieve the rows and columns you need, filter records, combine information from different tables, summarise results and, with the appropriate permissions, create or change data.
For anyone moving into data analysis, business intelligence or a data-heavy Business Analyst role, SQL is one of the most useful technical skills to learn. It sits between the data stored in operational systems and tools such as Excel and Power BI that people use to analyse and present that information.
What does SQL stand for?
SQL stands for Structured Query Language. It is commonly pronounced either “S-Q-L” or “sequel”. Both pronunciations are widely understood.
SQL is associated with relational database systems, where information is organised into tables containing rows and columns. Different database platforms use SQL with their own extensions and implementation details.
Microsoft SQL Server, for example, uses Transact-SQL (T-SQL), Microsoft's extension of SQL. Other widely used platforms include PostgreSQL, MySQL, Oracle Database and cloud database services.
What is a relational database?
A relational database stores related information in separate tables rather than repeating everything in one enormous dataset.
Imagine a training company that stores information about:
- customers
- courses
- course events
- bookings
- delegates
- invoices
A booking table might contain a Customer ID and Event ID rather than repeating every customer address and every course description on every booking record. Relationships between the tables allow the database to bring the information together when it is needed.
This structure reduces unnecessary duplication and helps maintain consistency.
What is a SQL query?
A query is an instruction asking the database to return or manipulate information.
One of the most common SQL statements is SELECT. Microsoft describes SELECT as the statement used to retrieve rows from a database, allowing you to choose columns and rows from one or more tables.
A very simple query might look like:
SELECT CourseName, Duration FROM Courses;
This asks the database to return the CourseName and Duration columns from the Courses table.
A real query can become much more sophisticated by adding filters, sorting, grouping, joins, calculations and other clauses.
Why is SQL useful for Data Analysts?
Data Analysts regularly need information that is too large, too detailed or too frequently changing to manage efficiently by copying data manually into spreadsheets.
SQL allows an analyst to work closer to the source and retrieve exactly the data needed for analysis.
A Data Analyst might use SQL to:
- retrieve sales transactions for a particular period
- combine customer and order information
- identify customers who have not purchased recently
- calculate revenue by product or region
- find duplicate or missing records
- create datasets for Power BI
- investigate unexpected trends
- check the data behind a dashboard
- produce reusable queries for recurring reporting
This is why SQL frequently appears alongside Excel and Power BI in Data Analyst job descriptions.
Why is SQL useful for Power BI?
Power BI can connect directly to many database platforms, including SQL Server. Understanding SQL helps a Power BI developer understand how source data is organised and how to retrieve it efficiently.
SQL can be useful before Power Query and DAX enter the workflow:
- The database stores operational data.
- SQL retrieves the relevant records.
- Power Query performs additional cleaning and transformation where needed.
- The Power BI semantic model creates relationships between analytical tables.
- DAX measures perform calculations that respond to report filters.
- Power BI visuals present the results.
These tools complement rather than replace one another.
For more about the calculation layer, see What Is DAX in Power BI?.
SQL vs Excel
Excel and SQL solve different problems.
Excel is excellent for interactive calculations, ad hoc analysis, modelling, charts and smaller datasets that users need to manipulate directly.
SQL is designed for structured interaction with databases. It is particularly useful when information is stored across multiple related tables or when the source contains far more rows than would be convenient to handle manually in a spreadsheet.
In practice, analysts often use both. SQL retrieves and shapes the source data, then Excel is used for analysis or presentation.
SQL vs Power Query
SQL and Power Query overlap in some data-preparation scenarios, but they work in different environments.
SQL runs against a relational database and can filter, join, group and transform information before it leaves the database.
Power Query is Microsoft's data connection and transformation technology used in tools including Excel and Power BI. It can combine database data with spreadsheets, files, web sources and many other systems.
Where a database can perform a transformation efficiently, doing appropriate work at the source can reduce the amount of data that needs to be transferred and processed later.
The core SQL concepts beginners should learn
SELECT
SELECT defines which columns or expressions should be returned.
SELECT CustomerName, City FROM Customers;
FROM
FROM identifies the table or source from which the query retrieves data.
WHERE
WHERE filters rows according to a condition.
SELECT CustomerName FROM Customers WHERE City = 'London';
This returns customers whose City value is London.
ORDER BY
ORDER BY sorts the query results.
You might sort course events by start date, sales by descending value or customers alphabetically.
DISTINCT
DISTINCT removes duplicate combinations from the result set. It can be useful when you want a list of unique values such as customer regions or product categories.
GROUP BY
GROUP BY allows rows to be grouped so aggregate calculations can be performed for each group.
For example, an analyst might calculate total sales by region rather than return every individual transaction.
Aggregate functions
Common aggregate functions include:
SUMfor totalsAVGfor averagesCOUNTfor countsMINfor minimum valuesMAXfor maximum values
JOIN
Joins combine related information from different tables. This is one of the most important SQL skills because relational databases deliberately store different entities separately.
What is a SQL JOIN?
Suppose you have a Customers table and an Orders table. Each order contains a Customer ID that identifies the customer who placed it.
A join can return columns from both tables so a report shows the customer's name alongside each order.
Common join types include:
- INNER JOIN: returns rows where the join condition matches in both sources.
- LEFT JOIN: returns all rows from the left-hand source and matching rows from the right-hand source.
- RIGHT JOIN: returns all rows from the right-hand source and matching rows from the left-hand source.
- FULL OUTER JOIN: returns matching and unmatched rows from both sides where supported.
Understanding joins is a major step from basic querying towards useful business analysis.
A simple SQL business example
Imagine you need to find total confirmed booking revenue by course category during the current year.
The relevant data may be stored across:
- Bookings
- CourseEvents
- Courses
- Categories
A SQL query could join those tables, filter bookings to the correct status and date range, group them by category and calculate the total booking value.
Without SQL, someone might export several files and attempt to combine them manually. With a well-designed query, the same logic can be rerun whenever updated figures are needed.
What is T-SQL?
T-SQL, or Transact-SQL, is Microsoft's implementation and extension of SQL. It is used with technologies including SQL Server and Azure SQL and also appears in Microsoft Fabric SQL experiences.
T-SQL includes the standard querying concepts beginners expect, such as SELECT, FROM, WHERE, GROUP BY, HAVING and ORDER BY, together with Microsoft-specific capabilities.
ExperTrain's Querying Data with Microsoft Transact-SQL course focuses on practical T-SQL querying skills.
Do you need to be a programmer to learn SQL?
No. SQL is a technical language, but beginner querying is approachable for people who do not consider themselves programmers.
SQL is declarative. In many cases, you describe the result you want rather than writing a complete step-by-step program explaining how the database engine should produce it.
The challenge is often less about syntax and more about understanding the data:
- Which table contains the information?
- How are tables related?
- What does each field mean?
- Which rows should be included?
- At what level should the result be grouped?
Those questions make SQL particularly relevant to analysts who already think carefully about business information.
Is SQL difficult to learn?
The fundamentals can be learned relatively quickly. Simple SELECT, WHERE and ORDER BY queries are straightforward.
The learning curve becomes steeper when you move into:
- multiple joins
- subqueries
- common table expressions
- window functions
- complex aggregation
- date logic
- query performance
- database design
You do not need all of those skills before SQL becomes useful. A modest set of querying techniques can already save significant time.
Does SQL have to be written in uppercase?
No. SQL keywords are often written in uppercase because it makes queries easier to read, but this is normally a style convention rather than a requirement.
Database behaviour around the case sensitivity of object names and data values depends on the platform and configuration.
Can SQL change data?
Yes. SQL is not limited to reading information. Depending on your permissions and the database system, SQL can insert, update and delete records and can also define database objects.
For analysts, read-only query access is common because operational data should be protected from accidental changes. Beginners should always understand what permissions they have and whether a statement modifies data before running it against a live environment.
What is NULL in SQL?
NULL represents the absence of a known value. It is not the same as zero, an empty string or the word “NULL”.
Understanding NULL is important because missing values affect filtering, joins and calculations in ways that can surprise beginners.
What is a primary key?
A primary key uniquely identifies a record in a table.
For example, CustomerID might uniquely identify each customer even if two customers have the same name. Keys are important when relating tables and avoiding ambiguity.
What is a foreign key?
A foreign key stores a value that relates a record to another table. An Orders table might contain CustomerID as a foreign key linking each order to the appropriate customer record.
Understanding primary and foreign keys makes joins and relational data much easier to understand.
SQL and data modelling
Learning SQL often improves a learner's understanding of how data is structured. Concepts such as tables, keys, relationships and data types also help when building Power BI semantic models.
Operational relational databases and analytical Power BI models are not identical, however. Power BI commonly benefits from a dimensional approach such as a star schema, where fact tables store events or measurements and dimension tables provide context.
See Power BI Star Schema Explained for a beginner-friendly introduction.
Which jobs benefit from SQL?
SQL is useful across many roles, including:
- Data Analyst
- Business Intelligence Analyst
- Power BI Developer
- Business Analyst
- Database Developer
- Data Engineer
- Software Developer
- Product Analyst
- Marketing Analyst
- Finance Analyst
- Operations Analyst
The depth required varies. A Business Analyst may only need enough SQL to investigate data independently, while a Data Engineer may need much more advanced database knowledge.
How should a beginner learn SQL?
A sensible learning sequence is:
- Understand tables, rows, columns and keys.
- Learn SELECT and FROM.
- Add filters with WHERE.
- Sort results with ORDER BY.
- Use aggregate functions such as SUM, COUNT and AVG.
- Group results with GROUP BY.
- Learn INNER JOIN and LEFT JOIN.
- Practise with realistic multi-table data.
- Move into subqueries, common table expressions and window functions when the basics are comfortable.
The most important part is practice. SQL makes much more sense when you use it to answer real questions rather than memorising syntax in isolation.
Frequently asked questions
Is SQL still worth learning?
Yes. Relational databases remain fundamental to business systems, analytics and cloud data platforms. SQL skills also transfer between multiple database technologies even though individual dialects differ.
Should I learn SQL before Power BI?
SQL is not required before starting Power BI. Many Power BI users begin with Excel files and other sources. SQL becomes increasingly valuable when your reporting depends on relational databases or you move towards professional Data Analyst and BI roles.
Should I learn SQL before Python?
For many business Data Analyst roles, SQL is the more immediately useful first language because so much organisational data is stored in relational databases. Python becomes valuable for broader automation, advanced analytics and data science. The best sequence depends on the role you are targeting.
Is SQL the same on every database?
The core concepts are highly transferable, but platforms have different dialects, functions, data types and extensions. T-SQL is Microsoft's SQL dialect.
Can SQL be used with Microsoft Fabric?
Yes. Microsoft Fabric includes SQL-based analytics and warehouse experiences, so SQL remains relevant within Microsoft's modern data platform.
Develop your SQL and data skills with ExperTrain
ExperTrain's instructor-led Querying Data with Microsoft Transact-SQL course develops practical skills in retrieving, filtering, joining and working with relational data using Microsoft's SQL language.
You can also explore our wider Data Analytics training, use the Data Analytics Glossary, or continue your learning with our Power BI Desktop Introduction and Power BI Desktop Intermediate courses.
For related reading, see Data Analyst vs Business Analyst and Power Query vs Power Pivot.
Further reading
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