TLDR;
This YouTube video provides a comprehensive SQL course, starting from basic concepts and progressing to advanced techniques, with a focus on practical application through projects. The course is designed for data engineers, data analysts, data scientists, and students, and it covers topics such as querying data, defining database structures, data manipulation, filtering, joins, functions, performance optimization, and AI integration. The course also includes hands-on projects like building a data warehouse and performing exploratory data analysis.
- SQL basics and setup
- Intermediate SQL techniques like filtering and joins
- Advanced SQL concepts such as window functions and stored procedures
- Performance optimization and AI integration
- Practical SQL projects for real-world application
Intro [0:00]
The course aims to teach not only SQL coding but also the underlying mechanisms of SQL, using animated visuals for easier understanding. The instructor shares industrial experience, best practices, tips, and decision-making processes in SQL. The course covers basics like writing SQL queries and advances to techniques such as window functions, stored procedures, and building a data warehouse. All materials, including code, presentations, and animations, are provided for free.
Introduction to SQL [7:38]
SQL is a language used to communicate with databases, which are organized containers for storing data. Databases are essential for companies to handle and organize massive amounts of data, offering advantages over simple files like efficient data access, management, and security. SQL enables users to ask questions and retrieve results from the database. A database management system (DBMS) is a software that manages database requests, security, and execution priorities. Real companies use servers, which are powerful PCs, to host databases, either within the company or through cloud services. There are different types of databases, including relational (SQL) and NoSQL databases, with this course focusing on SQL relational databases like Microsoft SQL Server. SQL commands are categorized into Data Definition Language (DDL), Data Manipulation Language (DML), and Data Query Language, each serving different purposes in defining, manipulating, and querying data. SQL is highly relevant due to its widespread use in companies and its adoption as an industry standard in modern data platforms and tools.
Setup Your Environment [22:33]
To set up the SQL learning environment, users are directed to a newsletter website to subscribe and download course materials, including datasets, documentation, and scripts. The guide provides instructions on downloading and installing SQL Server Express, a free edition suitable for practicing SQL, and SQL Server Management Studio (SSMS), a client tool for interacting with SQL Server. Users are shown how to connect to the SQL Server using Windows authentication and how to create databases using SQL scripts or by restoring a backup file. The process includes creating two databases, "my database" and "sales DB," and importing the Adventure Works database as an additional resource.
Query Data (SELECT) [34:01]
An SQL query is a request to retrieve data from a database, using the SELECT statement to specify the columns to be retrieved and the FROM clause to indicate the table. The SELECT * statement retrieves all columns from a table. SQL executes the FROM clause first, then the SELECT clause. Comments can be added to SQL code using two dashes for inline comments or /* and */ for multi-line comments. The WHERE clause is used to filter data based on a condition, with comparison operators like =, !=, >, <, and logical operators. String values in conditions must be enclosed in single quotes. The ORDER BY clause sorts the result set, either ascending (ASC) or descending (DESC), and can sort by multiple columns, creating a nested sorting order. The GROUP BY clause combines rows with the same value in a specified column, often used with aggregate functions like SUM to perform calculations on grouped data. The HAVING clause filters data after aggregation, based on conditions applied to aggregated values. The DISTINCT keyword removes duplicate rows from the result set, ensuring each value appears only once. The TOP keyword limits the number of rows returned in the result set, useful for retrieving a specific number of records.
DDL Commands [1:32:31]
DDL commands are used to define the structure of a database. The CREATE command is used to create new objects, such as tables. The syntax for creating a table includes specifying the table name, column names, data types, and constraints like NOT NULL. A primary key constraint is essential for uniquely identifying each row in a table. The ALTER command is used to modify existing database objects, such as adding or removing columns from a table. The DROP command is used to delete database objects, such as tables.
DML Commands [1:43:44]
DML commands are used to manipulate data within a database. The INSERT command adds new rows to a table, requiring specification of the table name and values for each column. The UPDATE command modifies existing data in a table, using the SET clause to specify which columns to update and the WHERE clause to filter the rows to be updated. The DELETE command removes rows from a table, using the WHERE clause to specify which rows to delete.
Filtering Data [2:08:03]
Filtering data in SQL involves using comparison operators (=, !=, >, <, >=, <=), logical operators (AND, OR, NOT), the BETWEEN operator to specify a range, the LIKE operator for pattern matching, and the IN operator to check if a value exists in a list. These operators are used in the WHERE clause to create conditions that filter the data based on specific criteria.
SQL Joins (Basics) [2:47:57]
SQL joins are used to combine data from multiple tables based on a related column. The basic types of joins include INNER JOIN, which returns only matching rows from both tables; FULL JOIN, which returns all rows from both tables, filling in NULL values for non-matching columns; LEFT JOIN, which returns all rows from the left table and matching rows from the right table, filling in NULL values for non-matching columns; and RIGHT JOIN, which returns all rows from the right table and matching rows from the left table, filling in NULL values for non-matching columns.
SQL Joins (Advanced) [3:27:29]
Advanced SQL joins involve understanding how to choose the right join type for a specific scenario. The choice depends on whether you need all rows from one table, only matching rows, or all rows from both tables.
Set Operators [4:02:09]
Set operators combine the results of multiple SELECT statements into a single result set. The UNION operator combines rows from multiple tables, removing duplicates. The UNION ALL operator combines rows from multiple tables, including duplicates. The EXCEPT operator returns rows from the first SELECT statement that are not present in the second SELECT statement. The INTERSECT operator returns rows that are common to both SELECT statements.
SQL Functions [4:47:41]
SQL functions are built-in code blocks that accept an input value, process it, and return a result. They are categorized into single-row functions, which transform individual values, and aggregate functions, which perform calculations on multiple rows.
String Functions [4:52:58]
String functions manipulate text values. CONCAT combines multiple strings into one. UPPER converts a string to uppercase, while LOWER converts it to lowercase. REPLACE substitutes characters within a string. TRIM removes leading and trailing spaces. LENGTH calculates the number of characters in a string. LEFT and RIGHT extract characters from the beginning or end of a string, respectively, and SUBSTRING extracts a portion of a string from a specified position.
Numeric Functions [5:18:44]
Numeric functions perform calculations on numeric values. The ROUND function rounds a number to a specified number of decimal places. The ABS function returns the absolute value of a number, converting negative numbers to positive.
Date and Time Functions [5:22:48]
Date and time functions manipulate date and time values. The YEAR, MONTH, and DAY functions extract the year, month, and day from a date, respectively. The GETDATE function returns the current date and time. The DATEADD function adds a specified time interval to a date, while the DATEDIFF function calculates the difference between two dates. The EOMONTH function returns the last day of the month for a given date.
NULL Functions [6:59:06]
NULL functions handle null values in SQL. ISNULL replaces null values with a specified value. COALESCE returns the first non-null value from a list of expressions. NULLIF returns NULL if two expressions are equal, otherwise, it returns the first expression.
Case Statement [8:07:50]
The CASE statement allows conditional logic in SQL queries, evaluating conditions and returning a value when the first condition is met. It can be used to categorize data, map values, and handle nulls.
Aggregate Functions [8:43:36]
Aggregate functions perform calculations on multiple rows and return a single value. Common aggregate functions include COUNT, SUM, AVG, MIN, and MAX.
Window Functions Basics [8:50:11]
Window functions perform calculations on a subset of data without losing row-level details, using clauses like PARTITION BY to divide data into windows and ORDER BY to sort data within each window.
Window Aggregate [9:47:00]
Window aggregate functions, such as SUM, AVG, MIN, and MAX, perform calculations on a window of rows defined by the OVER clause, allowing for cumulative or moving calculations.
Window Ranking [10:53:09]
Window ranking functions assign a rank to each row within a window based on a specified order. Functions include ROW_NUMBER, RANK, DENSE_RANK, and NTILE.
Window Value [11:56:05]
Window value functions, such as LEAD and LAG, allow access to data from other rows within a window. LEAD accesses data from subsequent rows, while LAG accesses data from preceding rows. FIRST_VALUE and LAST_VALUE return the first and last values within a window, respectively.
Advanced SQL Techniques [12:40:34]
Advanced SQL techniques include subqueries, common table expressions (CTEs), views, and techniques for optimizing query performance.
Subqueries [12:58:04]
Subqueries are queries nested inside another query, used to perform complex filtering or calculations. They can be scalar (returning a single value), row (returning multiple rows and a single column), or table (returning multiple rows and multiple columns). Subqueries can be correlated (dependent on the outer query) or non-correlated (independent of the outer query).
Common Table Expressions (CTE) [14:18:08]
Common Table Expressions (CTEs) are temporary named result sets that can be used multiple times within a single query, improving readability and reducing redundancy. CTEs can be standalone or nested, and recursive CTEs allow for iterative processing of hierarchical data.
Views [15:35:02]
Views are virtual tables based on the result of a query, providing an abstraction layer between the physical data and the end-users. They are used to simplify complex queries, enforce security, and provide multi-language support.
CTAS and Temp Tables [16:36:40]
CTAS (Create Table As Select) creates a new table based on the result of a SELECT statement, while temporary tables store intermediate results during a session and are automatically dropped when the session ends.
Compare Advanced Techniques [17:17:31]
A comparison of subqueries, CTEs, views, CTAS, and temporary tables highlights their differences in storage, lifetime, query scope, reusability, and data freshness.
Stored Procedures [17:27:04]
Stored procedures are pre-compiled SQL code blocks stored in the database, allowing for reusability, modularity, and improved performance. They can accept parameters, declare variables, and include error handling.
Triggers [18:12:58]
Triggers are special stored procedures that automatically run in response to specific events on a table, such as INSERT, UPDATE, or DELETE. They are used to maintain audit logs and enforce data integrity.
Indexes [18:23:42]
Indexes are data structures that provide quick access to rows in a table, improving query performance. Types of indexes include clustered, non-clustered, row store, column store, unique, and filtered indexes.
Execution Plan [20:20:31]
An execution plan shows how the database processes a query step by step, helping to identify performance bottlenecks and optimize query performance.
Partitions [21:11:03]
Partitions divide a large table into smaller, more manageable pieces, improving query performance and scalability.
30x Performance Tips [21:43:39]
Performance tips include selecting only necessary data, avoiding unnecessary DISTINCT and ORDER BY clauses, limiting rows during exploration, indexing frequently used columns in WHERE clauses, avoiding functions on indexed columns in WHERE clauses, using IN instead of multiple OR operators, filtering data before joining large tables, using UNION ALL instead of UNION when duplicates are acceptable, using column store indexes for aggregations on large tables, pre-aggregating data for reporting, and using SQL hints carefully.
AI and SQL [22:24:25]
AI tools like ChatGPT and GitHub Copilot can assist with SQL development tasks such as brainstorming, code generation, debugging, documentation, and code styling.
Project: SQL Data Warehouse [23:21:04]
The SQL Data Warehouse project involves building a modern data warehouse using SQL Server to consolidate sales data, enable analytical reporting, and inform decision-making. The project includes importing data from CRM and ERP systems, cleaning and fixing data quality issues, integrating data into a user-friendly data model, and providing clear documentation.
Project DWH | Bronze [24:32:54]
The Bronze layer focuses on data ingestion from CSV files into SQL Server, creating tables with specified columns and data types, and ensuring data completeness.
Project DWH | Silver [25:10:09]
The Silver layer involves data transformation and cleansing, including handling invalid values, data type casting, and data normalization.
Project DWH | Gold [26:47:46]
The Gold layer focuses on building a star schema data model, creating dimensions and facts, and generating reports for business users.
Project: Exploratory Data Analysis (EDA) [27:41:51]
The Exploratory Data Analysis (EDA) project involves understanding and uncovering insights about the dataset using basic SQL skills, asking the right questions, and finding answers through simple aggregations and techniques.
Project: Advanced Data Analytics [28:30:38]
The Advanced Data Analytics project involves using advanced SQL techniques to answer business questions, such as finding trends over time, comparing performance, and segmenting data.
Thank You [29:47:24]
The video concludes with a thank you message and a call to support the channel.