Relational databases integrate with other systems through standardized interfaces, data exchange formats, and middleware tools. The most common method involves using APIs (Application Programming Interfaces) that allow external systems to send and receive data through structured queries. For example, a web application might use RESTful APIs to communicate with a PostgreSQL database, executing SQL queries via HTTP requests. Middleware like ODBC (Open Database Connectivity) or JDBC (Java Database Connectivity) drivers enables programming languages to interact with databases regardless of the underlying platform. This abstraction allows systems written in Python, Java, or C# to connect seamlessly to databases like MySQL or Microsoft SQL Server.
Another key integration approach is through ETL (Extract, Transform, Load) processes, which move data between databases and external systems like data warehouses or analytics platforms. Tools like Apache Airflow or AWS Glue automate these workflows, extracting data from relational tables, transforming it into a compatible format (e.g., JSON or Parquet), and loading it into systems like Amazon Redshift or Snowflake. For instance, a retail company might use ETL to sync daily sales data from an operational MySQL database to a analytics warehouse for reporting. Stored procedures and triggers within the database itself can also push data to external systems, such as sending notifications to a message queue when a record is updated.
Security and performance considerations are critical during integration. APIs often use token-based authentication (OAuth) or SSL encryption to protect data in transit. ORMs (Object-Relational Mappers) like Entity Framework or Hibernate simplify data mapping between application objects and database tables but require careful tuning to avoid inefficient queries. Direct database links, such as PostgreSQL’s Foreign Data Wrappers, allow querying external databases as if they were local tables, enabling cross-system joins. However, schema changes in the database (e.g., adding a column) can break integrations if not communicated to dependent systems. Developers must balance tight coupling for real-time needs (e.g., banking transactions) with asynchronous patterns (e.g., event sourcing) for scalability. For example, a microservices architecture might use a relational database per service, with events published via Kafka to synchronize data across domains.