Key things to consider while working in microservice environment are: 1. It's a distributed system, so expect the inherent Eventual Consistency. 2. Ensure your service handles At-Least-One delivery of messages, Idempotency check for all the APIs which creates an entity. 3. Also be aware of the CAP rule, and plan the error handling, and fail the write operations if the system is not in a healthy state. 4. I would highly recommend the Event-sourcing pattern to keep versions of every change, or immutable representation. Event-sourcing may not be a right pattern for every use-case, but can be used based on the domain and data model. 5. Avoid single point of failure of data stores, and ensure multi-az setup.

Monoliths have features that users love and know well. They are consistent. The ACID properties (atomic, consistent, isolated, and permanent) simply do not apply in a system with microservices. Instead, we have to deal with the consequences of "eventual consistency," which includes functional consequences. The foundation for this is the CAP theorem and the change from ACID to BASE (basic availability, soft state, and eventual consistency). The microservice architecture must ensure that the functional requirements for consistency are also met! Cooperation with the business experts during the creation of the new architecture is crucial for success.

Usando bilhetes em papel de pão com firma reconhecida em cartório com assinatura de todos os donos dos serviços ao mesmo tempo.

Moving to a microservices architecture brings numerous benefits in terms of scalability, flexibility, and maintainability. However, ensuring data consistency and integrity in a distributed environment can present significant challenges. Moving to a microservices architecture means moving to small, independent services that communicate via APIs, although it complicates data consistency and integrity due to each microservice's database. Ensuring data consistency and integrity in a microservices architecture requires careful planning and adopting appropriate architectural patterns. By combining design techniques, appropriate tools, and best practices, you can build robust and reliable distributed systems that maintain data integrity.

To ensure data consistency and integrity during a transition to microservices architecture, I’d implement the following strategies: 1. Event Sourcing: Capture changes as events, allowing for a historical log that ensures data integrity. 2. Sagas: Use distributed transaction patterns like sagas to manage service workflow, maintaining consistency. 3. Database per Service: Each service should manage its data independently, reducing dependencies. 4. Data Replication & Sync: Set up data replication and sync mechanisms for frequently shared data. 5. Regular Audits: Perform data audits and validation to detect and correct any discrepancies early. An additional focus on CAP theorem (Consistency, Availability, and Partition Tolerance)

When moving to microservices and ensuring data consistency can be achieved my using message brokers like rabbit mq, which help to asynchronously process request only once and keep the transaction idempotent.

Key Points to remember: - SAGA Orchestration and/or SAGA Choreography - Reconciliation across Datastores - Trust your intuition and learnings from Past experience on Distributed design

Afin d'assurer la cohérence et l'intégrité des données dans une architecture de microservices, il est envisageable de recourir au modèle Saga pour gérer les transactions distribuées et d'établir une certaine cohérence avec des mécanismes de synchronisation. De plus, il est nécessaire que chaque microservice possède sa propre base de données, ce qui diminue la connexion tout en exigant des validations rigoureuses et des tests automatisés.

Por envolver sistemas distribuídos, haverão problemas de transação e, consequentemente, consistências eventuais. Por isso, é necessário analisar a importância dos dados e uso deles atraves da teoria CAP e trade-offs que o(s) time(s) definir. Segue algumas dicas a mais para auxiliar: - Implementar alguns padrões como Saga, Retry, Event-Driven e CQRS auxiliam na migração. - Há de pesar a experiência do time com microservices e seus padrões. Um time com pouco conhecimento pode dificultar a migração e não implementar padrões de forma "correta" ao contexto. - Pensar em idempotencia nas operações a serem realizadas podem resolver possíveis problemas de inconsistência e rastreamento destas e os dados utilizados.