Database Fundamentals for Backend Development

Databases power dynamic web apps: choose RDBMS for structured, ACID-compliant data (e.g., MySQL for transactions) or NoSQL for scalable flexibility (e.g., MongoDB). ACID ensures reliability, while CAP guides distributed trade-offs—prioritize CA for prototypes, AP for high-traffic sites. Master these to build robust backends complementing your frontend skills.

The relational model stores data in tables made up of rows and columns. Primary keys uniquely identify records, foreign keys define relationships between tables, and constraints enforce data integrity rules.

Normalization organizes database tables to reduce redundancy and maintain consistency.
1NF removes repeating groups, 2NF eliminates partial dependencies, and 3NF removes transitive dependencies. Applying these principles helps avoid insert, update, and delete anomalies, leading to cleaner and more reliable backend data design.

DDL commands define database structure. CREATE is used to add tables, indexes, and views, ALTER modifies existing objects, and DROP removes them. Together, these operations enable effective schema design and maintenance in backend systems.

DML commands handle data inside tables. INSERT adds new records, UPDATE modifies existing data, DELETE removes records, and SELECT retrieves data. Clauses such as WHERE, ORDER BY, and LIMIT allow filtering, sorting, and restricting query results, making data access efficient and controlled.

Query optimization helps databases run queries faster and more efficiently. EXPLAIN plans reveal how queries are executed, while basic indexing strategies reduce full table scans and speed up data access. Together, they form the foundation of effective database performance tuning.

Joins combine data from multiple tables. INNER joins return only matching records, LEFT and RIGHT joins include unmatched records from one side, FULL joins include all records from both tables, and self-joins relate a table to itself. These join types enable flexible and powerful data retrieval in relational databases.

Aggregations and subqueries help summarize and analyze data. GROUP BY and HAVING aggregate and filter grouped results, EXISTS enables conditional logic with subqueries, and window functions like ROW_NUMBER and RANK provide ranking and ordering without losing row-level detail.

Stored procedures, functions, and triggers enable reusable and automated logic inside the database. Procedures handle multi-step operations, functions return computed values, and triggers respond automatically to data changes. Together, they help maintain consistency, efficiency, and integrity in backend systems.

ACID transactions guarantee data reliability. BEGIN starts a transaction, COMMIT permanently saves changes, ROLLBACK reverts them on failure, and savepoints enable partial rollbacks. Together, they protect databases from inconsistency and errors in backend applications.

Concurrency issues occur when transactions run simultaneously. Lost updates and dirty reads can corrupt data if not controlled. Isolation levels like READ COMMITTED and SERIALIZABLE manage transaction visibility and consistency, helping backend systems balance accuracy and performance.

Locking mechanisms protect data consistency in concurrent environments by controlling access to shared resources. Deadlocks occur when transactions block each other indefinitely. Proper locking strategies and deadlock prevention techniques help maintain performance and reliability in multi-user backend systems.

ER diagrams visually represent database structure using entities, attributes, and relationships. They guide schema design, helping create organized, consistent, and maintainable databases for backend applications.

Indexing strategies improve query performance by enabling faster data retrieval. Properly indexing commonly queried columns reduces lookup time while maintaining a balance between read efficiency, write cost, and storage usage in backend databases.

Partitioning and sharding divide large datasets into smaller units to improve performance and scalability. Partitioning splits data within a database, while sharding distributes data across multiple databases or servers. Both techniques are essential for building scalable backend systems.

Connection management improves database performance and scalability. Connection pooling reuses database connections efficiently, while ORMs like SQLAlchemy, Prisma, and Sequelize abstract database access and manage connections automatically. Together, they help build scalable and maintainable backend systems.

Query building can be done using raw SQL for maximum control or ORM queries for productivity and safety. Migration tools like Alembic and Django migrations handle schema changes reliably, supporting consistent and maintainable backend database development.

Performance tuning improves backend scalability and response times. Redis caching reduces repeated database queries, read replicas balance read workloads, and effective connection lifecycle management prevents resource exhaustion. Together, these techniques help build fast and scalable backend systems.

Redis and MongoDB are NoSQL databases used for performance and flexibility. Redis excels at fast in-memory caching and real-time operations, while MongoDB supports flexible, document-based schemas

Cassandra and Neo4j address different backend data needs. Cassandra supports scalable, distributed column-family storage for high-volume workloads, while Neo4j excels at modeling and querying complex relationships. Both enable efficient handling of specialized backend data use cases.

Hybrid database architectures combine SQL and NoSQL systems. SQL is used for structured, transactional data, while NoSQL handles scalable, flexible, or high-performance workloads. Using both allows backend systems to balance consistency, scalability, and efficiency effectively.

Authentication confirms user identity, and authorization controls access rights. User roles simplify permission management, while Row-Level Security enforces fine-grained access at the data level. Together, they strengthen security in backend database systems.

Database security involves preventing SQL injection, encrypting data, and tracking access. SQL injection prevention protects against malicious queries, encryption secures data at rest and in transit, and auditing provides visibility and accountability. Together, these measures ensure secure and trustworthy backend systems.

Backup and recovery protect databases from data loss. WAL ensures crash safety, point-in-time recovery enables precise restoration, and tools like pgBadger support monitoring and performance analysis. Together, they ensure reliability and observability in backend databases.