Learn SQL +Security(pen) testing from Scratch

Learn the basics of databases, tables, and records, and how SQL enables querying, inserting, updating, and deleting data across multiple tables.

Discover how database management systems execute SQL queries across MySQL, Oracle, SQL Server, and SQLite, and learn why 90% of SQL is common.

Download and install the MySQL server and the editor workbench, then configure a root user and port 3306 to connect to your database.

Connect to the MySQL server using the workbench, explore the world database and its city table, and write SQL queries to retrieve data.

Create a database using SQL commands, then set up and test tables in a SQL workbench, verify creation via logs, and prepare data testing for the new database.

Point to a database with use command, then create a table employee info with columns name, id, location, and age, using varchar for names and integers for id and age.

Explore SQL table schema using describe, learn about data types and constraints such as not null and default values, and insert records with correct quotes for testing.

Master the basics of retrieving records in sql by using select from, choosing specific fields or all columns with star, and distinguishing describe from select for table schema and data.

Learn to add and modify a table column using SQL by altering the employee info table to include gender, changing data types, and observing effects like zero rows affected.

Drop a column from a table using alter table and verify the deletion with describe, while noting that DDL alters schema, not data.

Learn how to update data in a table using the sql update command, including setting a column value and using where clauses to target rows such as name = Sam.

Learn to delete rows with where conditions, update data, and modify table structures using drop, alter, and drop database commands, and use a sample database.

Discover how to use the select statement to retrieve data, choose specific columns or all columns, and apply distinct to get unique names from the employee info table.

Learn how to construct SQL queries using the where clause to filter employees by location and age, combining conditions with and or operators.

Explore sql logical operators and or, learn to use brackets for correct precedence, and apply the in operator to filter by age ranges and locations.

Master the between and not between SQL operators to filter data, using age and id ranges and the in operator for concise, efficient queries.

Discover essential SQL operators such as where, and, or, in, between, not, and group by, plus the basics of regular expressions using percentile and underscore for pattern matching.

Explore wildcards in SQL using the like operator with percent and underscore, applying simple regular expression patterns to filter city and country data by starting or specific letter positions.

Learn to sort query results using the order by clause, choosing columns like name or id, and switch to descending order with desc to control result order.

Learn how group by and aggregate functions in SQL consolidate transactions by month, calculating monthly totals and understanding group-based results through practical examples.

Discover how to use group by with aggregate functions max, min, and avg, to find monthly maximum transactions, per-branch minimums, and overall averages.

Learn how to apply the having clause with group by to filter grouped aggregates, using count and other aggregate functions for each month of transactions.

Compare where and having with group by to filter monthly sums for Bangalore and Chicago, excluding Sydney and Paris, using queries that apply before and after grouping.

Apply where and aggregate functions to group by month, filtering for Sydney and Paris branches, and use having to spot months with total sales under 1000.

Master SQL querying with practical exercises on group by, where, having, and aggregate functions to analyze February branch transactions and identify totals above 4000.

Learn to use subqueries to fetch ages by location across the citizens and citizen location tables, using a common column like name.

Master subqueries by comparing single and multiple results, using equal and in operators, linking tables through common columns, and retrieving name, location, and age.

Discover how joins merge two tables on a common column to produce a single table, enabling a single query instead of multiple subqueries.

Learn to join two tables, citizens and citizen location, on the name column. Select name, location, and age using aliases like c and c1, and note that joins outperform subqueries.

Learn how to join two tables and use group by and having to compute the total employee count for companies established in 1990.

Master joins and subqueries in SQL by building step-by-step queries to count employees per company, use sum and having for year 1990, and explore min, max, and filters.

Demonstrate how inner, left, and right joins connect student details with exam results by ID, showing how a left join returns all appeared students with null grades.

Learn how inner, left, and right joins work with real-time examples using student and grades data, including mapping, nulls, and practical query patterns.

Explore how views act as virtual tables derived from a parent table to hide confidential details and share Google data, demonstrated with create view and the google_info view.

Learn how to compute the world average employee count and list companies with above-average employees using a SQL view, with group by, having, and subqueries.

Explore SQL string functions, including concat to merge name and branch, and substring to extract specific text segments, and learn how to rename a column with as.

Explore SQL string functions including replace, length, trim, substring and concatenate, with practical examples; compare top and limit for retrieving rows across SQL Server and MySQL.

Explore string functions in SQL, including length and trim, with practical examples like select length(name) from company and trim of spaces; compare top and limit across SQL Server and MySQL.