Lesson 1
SQL, which stands for Structured Query Language, is a standardized programming language used for managing data in relational databases and performing various operations on that data.
The history of SQL dates back to the 1970s when it was developed at IBM by Donald D. Chamberlin and Raymond F. Boyce. Originally called SEQUEL (Structured English Query Language), SQL was designed to manipulate and retrieve data stored in IBM's original relational database system, System R. In the late 1970s, SEQUEL was renamed to SQL in order to avoid violations of international trademark laws.
In 1986, SQL became an ANSI standard, and later an ISO standard in 1987. This helped SQL solidify its position as the predominant language for database querying and management. Over the years SQL continued evolving with new versions and capabilities being added. Despite new versions, the core language has remained stable, which has greatly contributed to its wide adoption.
Today, SQL is supported by all major relational database management systems including Oracle, Microsoft SQL Server, MySQL, and PostgreSQL. It is widely used by database administrators as well as developers when writing data integration scripts. The simplicity and expressiveness of SQL syntax has been integral to its success.
At its core, SQL allows you to perform four broad types of operations on database data:
Query - Select and filter data to extract specific insights using SELECT statements.
Insert - Add new data records to the database using INSERT statements.
Update - Modify existing records in the database using UPDATE statements.
Delete - Remove records from the database using DELETE statements.
Beyond these basic functions, SQL also allows you to manage databases and database objects like tables, perform transaction control, and implement access control. Built-in functions and complex operators extend the flexibility of the language.
The ubiquity of relational databases across applications means that SQL skills are highly valued across industries. Whether you are a developer, data analyst, data scientist, or administrator, knowing SQL can help you efficiently access and work with data. Given the dominance of SQL across the data management landscape, it is likely to remain an essential skill for working with data for the foreseeable future.
Relational databases have become a predominant way of organizing and querying data across many applications and systems. In a relational database, data is stored in relations known as tables. Each table consists of rows and columns, with rows representing records and columns representing the attributes for that record. For example, a Customers table may have columns like CustomerID, Name, Address, and Phone Number. Each row would be a customer record with a unique ID and values for the name, address and phone number attributes.
Tables in a relational database have relationships between them. Typically, relationships are formed using foreign keys and primary keys. A foreign key in one table refers to the primary key of another table. For instance, an Orders table may have a CustomerID foreign key that references the primary key in the Customers table. This allows retrieving a customer's orders by joining the two tables on that key. These relationships allow efficient querying of an interconnected network of database tables.
Organized Structure: The tabular schema and defined relationships between tables enable efficient storage and querying. Developers can understand the structure intuitively.
Flexibility: Tables can be added or altered independently to change the data model. New relationships can be defined without disrupting existing ones.
Data Integrity: Features like primary keys, foreign keys and constraints ensure data validity and consistency. Duplication is avoided through normalization.
Scalability: Databases can scale to support larger datasets while maintaining performance through partitioning and replication.
Accessibility: The standardized SQL language provides simple, yet powerful ways to interact with relational data. This also enables abstraction of the underlying database system.
Transaction Support: Mechanisms for concurrency control, multi-statement transactions and rollback help maintain data consistency.
Security: Granular user permissions can be applied per table or column to control access. Network encryption protocols also protect from eavesdropping.
Backups: Mature backup and recovery tools allow periodic snapshots to prevent data loss.
These capabilities have made the relational model suitable for large-scale, enterprise database systems where complexity, performance and integrity are critical. The simplicity of the tabular structure also makes it accessible for personal databases and smaller scale projects. Alternate NoSQL database models can be better suited for some specific data types, but relational databases continue to be a versatile, trusted choice for general data management across domains.