Data integration patterns explained

Data integration patterns lie at the heart of modern data management. They help standardise the process of building a unified view of data from multiple sources. By thinking carefully about data integration design patterns, you can efficiently combine, process and maintain data across multiple systems. The result? Smoother operations and sharper decision-making capabilities.

Data integration architecture patterns will no doubt form part of your wider architectural choices. The decisions you make around microservice gateway and access patterns, for example, will need to be aligned with those you’re making regarding data integration models.

With that in mind, we will examine some of the top data integration patterns that could help you below, including migration, broadcast, bidirectional synchronisation, correlation and aggregation. Before we dive into the details, let’s look at precisely what data integration is and why it matters.

What is data integration?

Data integration is the process of combining data from different sources into a single, unified view. This process involves collecting data from various sources, including different databases, systems, and storage technologies. It then involves cleansing, organising and consolidating the data to make it more accessible and useful for human users and other applications.

As it enables you to make more informed decisions, based on comprehensive and up-to-date information, data integration is a critical component in the fields of business intelligence, data analytics and information management.

The primary goal of data integration is to provide a cohesive data environment that supports effective data analysis and decision-making processes. This involves several key activities.

Key activities in data integration

There are three important activities to be aware of in data integration: extraction, transformation and loading.

  • Extraction refers to collecting or retrieving data from its original sources. These can include databases, cloud storage, APIs and other data repositories.
  • Transformation involves converting the extracted data into a format or structure that is consistent and suitable for integration. This can involve cleansing data to remove inaccuracies, converting data types, normalising data formats and applying business rules.
  • Loading means storing the transformed data in a target system, such as a database, data warehouse, or data lake. From there, you can access and use the data for reporting, analysis, or further processing.

Techniques and approaches

Before we move on to integration design patterns, it’s worth understanding some of the techniques and approaches used in data integration. These help ensure your data is always available to those who need it in the most efficient manner.

Common data integration techniques and approaches include:

  • Extract, transform, load (ETL): a traditional data integration process where data is extracted, transformed and loaded in a sequence.
  • Extract, load, transform (ELT): a variation of ETL where data is first loaded into the target system before transformation. This approach is often used with modern data warehouses that can handle large-scale transformations.

Understanding these approaches can aid your decision-making when it comes to the design of your data architecture.

Benefits of data integration

Taking the right approach to your enterprise data integration patterns can deliver impressive rewards. You can achieve greater efficiency in your operations by automating data integration processes and reducing manual efforts and errors. You can enhance data quality by including processes for cleansing and standardising data when you design patterns for data integration, thus improving its accuracy and reliability.

Significantly, you can also use data integration patterns to achieve better customer insights. By integrating data from various customer touchpoints, you can glean deeper insights into customer behaviour and preferences. This, in turn, enables you to enhance customer service and targeting, which can lead to increased customer loyalty, lower churn rates and other important benefits.

At a strategic level, unified data can provide a comprehensive view of your business operations. This enables better decision-making and strategic planning.

Top data integration patterns

Data integration patterns refer to the standardised methods or approaches used to address common data integration challenges, such as combining data from disparate sources, ensuring data consistency and providing unified data views. These patterns are essential in designing efficient and scalable data integration solutions.

Whether you need data integration patterns for data warehouse automation, enhanced customer operations or any other reason, the following five integration patterns should meet your needs. We’ll discuss migration as a precursor to enhancing or merging systems, broadcast as a means of amplifying critical data across destinations in real-time and bi-directional sync, which acts like a two-way street ensuring consistent information flow between datasets. We’ll also explore how correlation patterns uncover unseen links and aggregation acts as a crucible, blending varied datasets together. 


Migration is the simplest data integration pattern you’re likely to need. It involves moving data from one point to another at a set point in time, filtering and transforming the data en route as required. You can migrate large volumes of data in this way. The migration is complete after you’ve checked the integrity of the data in the target system compared to that in the source system.  


Like migration, the broadcast integration pattern begins with a single data source. Unlike migration, however, broadcast involves several target systems. It covers the continuous, automatic broadcast of data to those systems in real-time, with no human involvement. Each broadcast can be scheduled or event-driven, with only the data that has changed since the previous update moving each time.

Bi-directional sync

If you need bidirectional integration, the bidirectional sync pattern should fit the bill. As the name implies, the data moves in two directions, flowing between two systems while delivering a unified, real-time view. This is ideal if you’re using different products as part of a custom-built suite of applications.


Correlation is essentially an ultra-efficient means of bidirectional integration. The syncing of the data takes place at the intersection of the two datasets, with only the data that is relevant to both systems synchronised (which is what makes it so delightfully efficient).


If you need a unified, real-time view of data from several systems, aggregation is likely to provide what you need. This integration pattern can gather and transform data from several sources at once, providing a comprehensive overview that can help inform strategic decision-making by keeping you fully informed in real-time.

Other useful data integration patterns and approaches

Depending on what you are trying to achieve, there are other useful integration patterns and approaches that you can use.

Batch data integration

This is the most traditional form of data integration, where data is collected, transformed and loaded at scheduled intervals. It’s suitable for scenarios where real-time data is not critical and the data volume is manageable within the batch window. This pattern is commonly implemented using ETL processes.

Real-time data integration

In contrast to batch processing, real-time data integration involves the continuous collection and integration of data as it becomes available. This pattern is essential for applications that require up-to-the-minute data, such as real-time analytics, monitoring systems, and online transaction processing. Techniques like change data capture (CDC) and event-driven architecture are often used.

Data consolidation

Data consolidation involves combining data from various sources into a single, centralised database or data warehouse. This pattern simplifies reporting and analysis by bringing all relevant data together, making it easier for businesses to gain insights. It’s particularly useful for creating a single source of truth within an organisation.

Data propagation

This pattern involves copying data from one location to another, either in real-time or at scheduled intervals, without necessarily transforming it. Data propagation can be synchronous or asynchronous and is often used for data backup, synchronisation and distributing information across different systems or locations.

Data federation

Data federation provides a unified view of data from multiple sources without physically moving or copying the data. It uses virtualisation technology to aggregate data in real-time from various sources, allowing users to query and analyse the data as if it were in a single database. This pattern is useful for accessing and combining data from siloed systems.

Data virtualisation

Closely related to data federation, data virtualisation involves creating a virtual layer that abstracts and integrates data from multiple sources, allowing for real-time access and analysis. This approach minimises data redundancy and latency, offering a flexible and efficient way to manage data integration.

API-led connectivity

As a microservices API gateway provider, Tyk has a deep-running appreciation of the advantages of API-led connectivity. In terms of data integration, this modern integration pattern uses APIs as the primary means of communication between different systems and data sources. It promotes the development of reusable and modular APIs, enabling more agile and scalable integration solutions. API-led connectivity is fundamental in microservices architectures and cloud-native applications.

Event-driven architecture (EDA)

In an EDA, data integration is triggered by events or changes in data rather than being scheduled at regular intervals. This pattern is highly responsive and efficient, as it minimises the need for polling and reduces latency. That means it’s ideal for applications that depend on real-time data updates and notifications.

Each pattern has its advantages and is suited to different use cases. Your data architecture decisions will depend on your specific requirements in terms of data volume, velocity and variety, and the need for real-time processing. The choice of pattern will significantly impact your architecture’s scalability, performance, and maintainability, so it is key to carefully consider application integration patterns before implementing them.

Mechanisms and techniques for effective data integration

To ensure you effectively achieve a unified view when using data integration patterns to combine data from different sources, formats and systems, we also need to mention enterprise application integration (EAI) and enterprise service bus (ESB) patterns.

EAI and ESB patterns provide the mechanisms and techniques necessary to integrate data from disparate systems effectively. They ensure that data can be exchanged, transformed, and routed seamlessly between different applications and services.

An EAI pattern provides a set of design principles and guidelines for integrating applications and systems effectively within an organisation. The focus is on integration at the data, application or business process level. Message transformation, routing, brokering, publishing/subscribing, and point-to-point integration are common EAI patterns.

ESB patterns focus on the design principles and mechanisms employed by an ESB model to facilitate the integration of applications and services by establishing communication between them. Common ESB patterns include service orchestration, service mediation, virtualisation, aggregation, and protocol bridging.


Data integration is no small feat, especially when juggling data across various systems, platforms and formats. The integration patterns, techniques and mechanisms we’ve discussed above provide plenty of flexibility in achieving what you need.

Of course, the larger and more complex your system, the greater the potential impact of changes to your data architecture. That can be a double-edged sword. On the one hand, a clumsy or rushed approach to any changes could result in data not flowing as it should, which can impact anything from internal services to customer-facing operations. On the other hand, getting your data architecture right can mean you reap all the rewards we mentioned, from greater efficiency and improved data quality to deeper insights and enhanced decision-making.

There’s something to be said for using the strangler fig pattern in such scenarios. It’s a long-established model for incrementally replacing legacy systems, but its approach of gradual, step-by-step changes makes sense in plenty of other scenarios.  

As with any changes designed to streamline your systems and enhance efficiency, be sure to map out your strategy and take things at a steady pace. Doing so should enable you to establish precisely which data integration patterns you need and how they will impact your business.

If you need any more information or want to explore other ways to achieve the ideal architecture for efficient business operations, why not talk to the Tyk team? We’re always available for a chat about all things technical, so feel free to get in touch to find out more.