Database Fundamentals

Understanding these core database concepts is essential for any developer working with data-driven applications. These fundamentals form the building blocks of modern database management systems.

Indexing – Speeds up read queries by letting the DB skip scanning full tables.
Primary Key – A unique identifier for each row; ensures entity integrity.
Foreign Key – Maintains relational integrity by linking to a primary key in another table.
Normalization – Process of structuring data to reduce redundancy and improve integrity.
Denormalization – Combining tables to reduce joins and improve read performance.
ACID Properties – Guarantees reliable transactions: Atomicity, Consistency, Isolation, Durability.
Isolation Levels – Control how concurrent transactions interact (e.g., Read Committed, Serializable).
Joins – Combine data from multiple tables; can be inner, left, right, or full outer.
Query Optimization – The process of improving query performance using execution plans and indexes.
Query Plan – A step-by-step roadmap the DB follows to execute your query.
EXPLAIN / ANALYZE – Tools to inspect and debug how your SQL query is being executed.
Sharding – Splits data across multiple machines to improve scalability.
Partitioning – Splits a single large table into smaller, manageable chunks.
Replication – Copies data from one DB node to another for fault tolerance and scaling reads.
CAP Theorem – States a distributed system can only guarantee two of Consistency, Availability, and Partition Tolerance.
Eventual Consistency – Guarantees data will sync across systems eventually, not instantly.
Deadlocks – When two transactions wait forever for each other to release locks.
Write-Ahead Logging (WAL) – Ensures data durability by logging changes before applying them.
MVCC (Multi-Version Concurrency Control) – Enables concurrent reads/writes without locking rows.
Connection Pooling – Reuses DB connections to reduce overhead and latency per request.
Materialized Views – Precomputed query results stored as a table to speed up expensive queries.
NULL Handling – Represents missing/unknown data, but behaves differently than empty or zero values.
Schema Migration – The process of safely changing a database schema without downtime or data loss.
Data Integrity - The accuracy, completeness, and consistency of data in a database.
Constraint – Rules like UNIQUE, CHECK, or NOT NULL that enforce data integrity at the schema level.

Database Types and Their Trade-offs

Different database types optimize for different aspects of the CAP theorem:

Relational Databases (RDBMS)

Oracle, MySQL, PostgreSQL, SQL Server
Prioritize ACID compliance and consistency
Excel at complex transactions and relations between entities
Use cases: Financial systems, ERP, CRM

NoSQL Document Stores

MongoDB, Couchbase
Prioritize flexibility and schema-less design
Excel at horizontal scaling
Use cases: Content management, catalogs, user profiles

Key-Value Stores

Redis, DynamoDB
Prioritize speed and simplicity
Excel at caching and high-throughput operations
Use cases: Session storage, caching, leaderboards

Wide-Column Stores

Cassandra, HBase
Prioritize high write throughput and horizontal scaling
Excel at time-series and big data applications
Use cases: IoT sensor data, weather data, analytics

Graph Databases

Neo4j, ArangoDB
Prioritize relationship modeling
Excel at connected data and traversal queries
Use cases: Social networks, recommendation engines, fraud detection

When to Use Which Database Strategy

Indexing: When query performance on specific columns is critical, but weigh against write performance costs
Normalization: When data consistency and integrity are paramount
Denormalization: When read performance needs to be optimized, especially in reporting systems
Sharding: When data volume exceeds what a single machine can handle
Materialized Views: When expensive calculations or aggregations are frequently queried

Conclusion

Understanding these database fundamentals allows developers to make informed decisions about data storage, access patterns, and system design. Whether building a simple application or a complex distributed system, these concepts provide the foundation for efficient and reliable data management.