Integration Patterns in Software Development

By Alok Mehta – CIO Business Systems.  Kemper Insurance. and Uma Medikondla – Director, Software Development.  Kemper Insurance

Applications are integrated with each other; without that we cannot meet users’ requirements. For example, when a user buys a pair of sunglasses on Amazon app, it must fulfill the order and bill the user when the sunglasses are delivered. In this case, the ordering system is integrated with the billing system and passes transactional data from the ordering system to the billing system, and confirmation from billing back to the ordering system. In technical terms, by integration, we mean passing data back and forth, so they meet users’ functional requirements along with meeting system’s non-functional requirements as well.  But how data is passed back and forth matters and depends on what the specific use case is.

In this article, we will discuss three common types (i.e. patterns) of integration, namely data-centric, event-driven and application-centric and their pros and cons.  Each one of these patterns can be used in a variety of use cases.

  1. Data-Centric Integration:

This is a strategy that revolves around establishing a single source of truth for data within an organization. This approach emphasizes accuracy, consistency, and reliability of data across various systems and applications. Below are some key aspects and common patterns associated with data-centric integration.

Focus on a Single Source of Truth:

The primary goal is to have a centralized and authoritative data repository, ensuring that all applications within the organization access consistent and accurate information.

Common patterns:

a. ETL (Extract, Transform, Load):

  • Description: Moves, and validates, data from diverse sources into a central repository, often a data warehouse or data lake.
  • Use Case: Useful for data consolidation, cleansing, and transformation before loading it into a target system.
  • Example: Data access from a data warehouse to data science team for analytics purposes

b. File Transfer:

  • Description: Securely transfers large datasets between systems, often in batch mode.
  • Use Case: Efficient for moving bulk data between applications and systems.
  • Example: An enterprise sending employee information to a payroll processing company.

c. Shared Database:

  • Description: Consolidates data into a single database accessible to multiple applications.
  • Use Case: Suitable for scenarios where multiple applications need to interact with and share a common set of data.
  • Example: Operational data store where customer transactions are processed and stored.

d. Data Replication:

  • Description: Maintains synchronized copies of data across multiple systems.
  • Use Case: Enables real-time or near-real-time access to data across distributed environments.
  • Example: Caching data for read-only purposes to provide status to the end use.

e. Aggregation:

  • Description: Gathers data from multiple sources for reporting and analytics.
  • Use Case: Useful for creating consolidated views of data for reporting and analysis purposes.
  • Example: Data lake, data warehouse.


  • Consistency: Ensures that all applications are using consistent and up-to-date data.
  • Efficiency: Reduces redundancy and enhances efficiency by avoiding duplicate data storage.
  • Accuracy: Facilitates accurate reporting and analytics by providing a reliable data foundation.

Key Considerations:

a. Data Quality and Consistency:

  • Ensuring data quality and consistency is crucial to maintain the integrity of the single source of truth.

b. Security and Compliance:

    • Implementing security measures to protect data during transfer and ensuring compliance with regulations.

c. Scalability:

    • Designing integration solutions that can scale to handle growing volumes of data and evolving business requirements.

d. Monitoring and Management:

    • Establishing monitoring mechanisms to track data movement, detect issues, and ensure timely resolution.

e. Metadata Management:

    • Managing metadata is essential for understanding the structure, origin, and lineage of the integrated data.

Data-centric Integration is fundamental for organizations aiming to leverage their data as a strategic asset, enabling informed decision making and enhancing overall operational efficiency.

  1. Event-Driven Integration:

This is a paradigm that focuses on enabling real-time responsiveness and agility in system architectures through loosely coupled communication. This approach is centered around the idea that systems can react to events as they occur, allowing for more dynamic and flexible interactions between components. Here are some key aspects and common patterns associated with event-driven integration.

Focus on Real-Time Responsiveness:

The primary goal is to create an architecture that responds to events as they happen, allowing systems to be more adaptive and responsive to changes in the environment.

Common patterns:

a. Message-Driven Communication:

  • Description: The approach centers around exchanging messages between components, representing commands, requests, or responses. It commonly employs message brokers for routing and delivery, but it involves a degree of coupling, as the sender expects specific responses, leading to more direct dependencies between components.
  • Use Case: Facilitated by brokers, decouples producers and consumers, supporting both sync/asynchronous models, and enhances responsiveness for real-time scenarios with improved scalability and adaptability.
  • Example: Most webpages work in this manner when a user orders something and the server provides periodic updates of the status of the order.

b. Event-Driven Communication:

  • Description: Broadcasts events on dedicated streams, allowing systems to react independently.
  • Use Case: Enables a reactive architecture where components can respond to events without direct dependencies on each other.
  • Example: Internet-of-things (IoT) thermostat sending periodic data to its mothership.


  • Real-time responsiveness: Enables systems to react in real-time to events, supporting more agile and responsive architectures.
  • Scalability: Asynchronous communication patterns facilitate better scalability by decoupling components.
  • Flexibility: Loosely coupled systems can evolve independently, promoting flexibility and adaptability.

Key Considerations:

a. Loose Coupling (defined as degree of connectedness between modules/applications):

  • Event-driven integration promotes loose coupling between components, allowing them to operate independently. Scalability:
  • Asynchronous communication patterns facilitate scalability, enabling systems to handle varying loads more efficiently.

b. Event Schema and Metadata:

  • Defining clear event schemas and metadata is crucial for understanding the nature and context of events.

c. Error Handling:

  • Implementing robust error-handling mechanisms, as events may be consumed by systems at different speeds.

d. Event Ordering:

  • Ensuring proper event ordering if sequential processing is critical for specific scenarios.

Event-driven integration is particularly valuable in scenarios where responsiveness to real-time events, scalability, and flexibility are critical requirements. It is widely used in modern architecture to support dynamic and distributed systems that need to react quickly to changing conditions in the environment.

  1. Application-Centric Integration:

This is an approach that focuses on promoting modularity, reusability, and maintainability in software applications through well-defined interfaces and APIs (Application Programming Interfaces). This integration paradigm emphasizes building applications as a set of loosely coupled, modular components that can communicate with each other seamlessly. Here are some key aspects and common patterns associated with application-centric integration:

Focus on Modularity and Reusability:

The primary goal is to design applications as a collection of independent and reusable components, each exposing well-defined interfaces.

Common patterns:

a. Facade:

  • Description: Simplifies external access to complex systems by providing a simplified interface.
  • Use Case: Offers a unified and simplified API to external systems, shielding them from the complexity of the underlying application.
  • Example: User interface of most portals like a financial system or an e-commerce platform.

b. Adapter:

  • Description: Translates data between systems with incompatible formats or interfaces.
  • Use Case: Enables interoperability between different systems by translating data formats or adapting interfaces.
  • Example: Integrating a modern payment gateway into an older e-commerce system.

c. Content-Based Routing:

  • Description: Intelligently routes requests based on their content, enabling targeted delivery.
  • Use Case: Allows for dynamic routing of requests to different components based on the content of the data, improving flexibility in handling diverse scenarios.
  • Example: An email filtering system may filter emails based on content in an email.


  • Modularity and Reusability: Encourages the development of modular and reusable components, leading to more maintainable and scalable applications.
  • Interoperability: Adapters facilitate communication between components with different interfaces, enabling seamless integration of diverse systems.
  • Simplified Access: Facades provide a simplified and consistent interface, making it easier for external systems to interact with complex applications.

Key Considerations:

a. Application Programming Interface (API) Design:

  • Well-designed APIs are crucial for effective communication between application components. Focus on simplicity, consistency, and ease of use.

b. Modularity:

  • Breaking down applications into modular components promotes reusability and simplifies maintenance.

c. Interoperability:

  • Adapters play a critical role in ensuring interoperability between systems with different data formats or interfaces.

d. Documentation:

  • Clear and comprehensive documentation for APIs and interfaces is essential for developers to understand how to interact with different components.

Application-centric integration is particularly relevant in modern software development, where building applications as a set of loosely coupled and independently deployable components is essential for scalability, maintainability, and agility. It aligns well with contemporary microservices (breaking larger problems into smaller chunks/services) architecture and API-driven development practices.

In summary, the integration of systems and applications in software development is crucial, with data-centric, event-driven, and application-centric integration each offering distinct benefits. The summary can be viewed in Figure 1.0.

Data-centric integration focuses on a single source of truth, ensuring data consistency and accuracy, ideal for scenarios requiring reliable data for decision making.

Event-driven integration excels in environments needing real-time responsiveness and scalability, perfect for dynamic, fast-changing conditions.

Application-centric integration emphasizes modularity and reusability, suited for scalable, maintainable applications, often aligning with microservices architecture and API-driven development.

The choice among these patterns depends on specific organizational needs and technological contexts. Each pattern plays a vital role in creating robust, efficient, and agile IT architectures, aligning with the strategic goals and operational demands of businesses. As technology evolves, so will these integration approaches, continually offering innovative solutions in software development.

Integration Type Primary Use Cases Key Benefits
Data-Centric Integration ·       Data consolidation, cleansing, transformation

·       Reporting and analytics

·       Real-time access to data across systems

·       Ensures data consistency and accuracy.

·       Reduces data redundancy.

·       Facilitates accurate reporting and analytics

Event-Driven Integration ·       Real-time system responsiveness

·       Decoupling of system components

·       Scalable communication in dynamic environments

·       Enables real-time responsiveness.

·       Promotes system scalability and flexibility.

·       Supports loosely coupled architectures

Application-Centric Integration ·       Modular application development

·       Interoperability between diverse systems

·       Simplifying complex system interactions

·       Encourages modularity and reusability.

·       Enhances interoperability.

·       Simplifies access to complex systems

Figure 1.0

Uma, Director of Software Development at Kemper Insurance, is a technology leader and architect involved in optimizing IT systems, refining business strategies, and implementing best engineering practices. Uma works with contemporary cloud technologies, user interactions, integrations, and databases; emphasizing DevSecOps practices, Generative AI adoption, and observability.