8 Data Management Patterns for Microservices

In the realm of software architecture, microservices stand out for their composition of loosely-coupled services, which can be deployed, scaled, and managed independently. It contrasts with conventional architecture that consists of one monolithic application. Common data management patterns for microservices include service discovery, which facilitates services to interact with each other, and data partitioning, which reduces complexity and improves scalability. Relationship management plays a role in this architecture, as it enables interaction among services. Distributed databases are favoured in microservice architecture, featuring multiple host computers to increase scalability and fault tolerance.

In case you are pondering over any of these questions, find the answers in this blog post.

Provide an explanation of microservices.

Microservices is a software development approach that ascribes to an architectural style of creating independent, modular units that can be merged to form a complete program. It promotes distributed data as each service can have its own database, offering greater scalability and flexibility.

An Application Programming Interface (API) facilitates communication between each component of the application and the different database services. It enables independent deployment and updating of each component without affecting others.

Patterns of microservices data management that I need to be aware of.

Data management strategies for microservices enable the seamless interaction of data between modules. They facilitate multiple software applications to communicate and exchange data, thereby improving the efficiency and effectiveness of information utilisation.

Explore the different data management patterns applied in microservices.

  1. Database organisation by service pattern

    Each microservice is responsible for managing its own data in the database, adhering to the service pattern’s architectural standards. Developers use Application Programming Interfaces (APIs) to enable communication when two or more microservices’ databases interact.

    Database-per-Service (DPS) design pattern streamlines database management, ideal for experimental software. DPS offers user-friendly impact analysis, low inter-database connectivity, and better scalability, which represent keys benefits of microservices. DPS has become a favoured choice for software development because of these advantages.
  2. Shared database structure

    Sharing access and storing data in a single database, the Shared Database Design allows multiple services to collaborate. When manipulating the database, it is crucial to exercise caution and avoid conflicts between different developers working on different parts of the system. Thus, handling the database necessitates awareness of potential runtime issues.

    The ACID (Atomicity, Consistency, Isolation, Durability) properties of transactions in a microservices paradigm with a shared database ensure data accuracy and integrity during transmission.
  3. Saga pattern in a cyclic format

    The Saga data management pattern arranges a series of database local transactions where the result of each is based on the outcome of the previous one. The completion of each transaction triggers an event.

    The Saga Pattern data management strategy empowers businesses to execute transactions involving multiple components with agility. In case of a flawed transaction, Saga enforces a series of remedial steps to successfully produce the desired data integrity and transaction completion.
  4. CQRS with repeatable pattern

    Command Query Responsibility Segregation (CQRS) is a popular microservice database management method. CQRS involves the capture of domain events from other microservices, and subsequently saving them in the database. Using this information, the query database or view is modified.

    CQRS is a software design pattern that allows microservices to operate more efficiently and rapidly by omitting calls to the original database. CQRS further improves productivity by eliminating traditional program layering strategies. With greater flexibility and reliability, this pattern offers a more agile and dependable platform that allows complex applications to scale and run faster.
  5. Event-driven architecture

    The event sourcing microservices architecture arranges data as a sequence of events that are organised and saved each time there is a change or addition. The event source generates a new event with each update.

    Event Sourcing is a pattern that allows for event tracking and replay. This feature is critical for constructing subscriptions and services accessible through Application Programming Interfaces (APIs). The ability to record and recall past events makes it possible to develop potent applications that offer customised services and experiences.
  6. Architectural Style for Application Programming Interfaces

    Data management patterns that rely on API composition provide API composers with immediate access to data sets that are optimised for specific services. This pattern involves collecting data, followed by the memory-based linkage of two services to produce the output for the client.

    The use of API composition can be highly advantageous for handling numerous microservice components and small data sets. However, for larger data sets, the in-memory join solution may not be the most efficient due to the potential for wasted resources.
  7. Domain-specific event patterns

    The Domain Event Pattern is an efficient tool for managing events specific to a given domain. The pattern facilitates information exchange between other bounded contexts, enables analysis of past data, ensures scalability for larger data sets, and promotes improved performance. Utilising the Domain Event Pattern allows organisations to effectively manage and track their domain-specific events, while also reaping the benefits of enhanced productivity and efficiency.
  8. Database design for partitioning

    The objective of the data sharding pattern is to facilitate the storage and accessibility of extensive data sets by segmenting them into smaller, more manageable fragments, also known as “shards.” Within microservice database management, each shard features the same schema, but only a subset of the total data.

    Implementation of sharding patterns offers superior scalability and enables organisations to meet increased storage requirements by adding new shards. Another method to enhance performance is to decrease the load on each service.


Integrating microservices data management strategies into software development has delivered many benefits, including improved performance, reliability, and operational efficiency. Additionally, this method simplifies testing, reducing the likelihood of errors.

When selecting a data management method, organisations should evaluate the software development project along with its microservices architecture. The level of experience that developers have with various data management techniques should also be taken into consideration, ensuring the team is sufficiently skilled to handle the project and implement the chosen data management approach.

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  1. How does microservices design differ from traditional architecture?

    Microservices design relies on containers, which provide autonomous software modules, and a service mesh for inter-service communication. Administrative tasks and load balancing are handled by an Application Programming Interface (API) gateway, while service discovery enables service deployment and load distribution. These components are crucial for achieving a proficient microservices architecture.
  2. What is the relationship management requirement in microservices?

    Microservices connections can be managed using API calls or asynchronous messaging-based database management strategies.
  3. How do microservices databases differ from each other?

    Both relational and non-relational databases can be used effectively with microservices.

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