How can you use the "Event Sourcing" cloud pattern to ensure data consistency?

Event Sourcing is not a "cloud pattern". Ok, I'll add more text to this post because there's a 750 minimum character requirement.

There are 2 parts to event sourcing The “event” object - that captures every change to the state of an application mostly with regards to a user The “ledger” - that stores event objects in the sequence they were applied for the same lifetime as the application state itself. Though it looks very similar to a blockchain - Event Sourcing may be a better suited approach to building blockchains. I wish I could add a diagram to this article.

Event Sourcing is a design pattern where changes to an application's state are captured as a series of immutable events. These events are stored in a log or sequence and can be used to reconstruct the state of the application at any point in time However, while Event Sourcing provides significant benefits for ensuring data consistency, it's important to note that implementing it correctly requires careful consideration of the system's complexity, event modeling, and infrastructure for managing event streams. Also, resolving eventual consistency across distributed systems and managing event streams in real-time can pose challenges that need to be addressed.

Event Sourcing is a clever way to use data in the cloud, and it's really useful for different business needs. 1: You can rewind time: Imagine you have a time machine for your data. With Event Sourcing, you can look back at any point in the past to see what your application looked like. It's like playing a video in reverse!? 2: No more data fights: Event sourcing mitigates concurrent updates that can lead to data conflicts and inconsistencies, you can handle data through event sequencing. 3: You can create different projections or views of your data by replaying events in various ways. This enables you to serve different use cases without modifying the source data, providing flexibility and consistency

If you want to think about event sourcing in real life, imagine you have your birthday party, and you recorded this party, so every event that happened at this party can be watched. You can also check how your house was during the party and how it moved from point A (Clean house) to point B (Chaotic house). Consider your system state as your house, and the events that happened at this party represent the event sourcing and your video recording is the event log.

It relies on an event store, a robust repository capturing every critical moment in an app's life. This write-only structure is the go-to place for understanding the app's state—past, present, and potential future. It's not just passive storage; it powers the app's logic, offering insights into current and historical states while allowing new events to be added, signifying changes in the app's course. It actively engages with other systems, sharing these events for analytics, notifications, or workflows that react to app changes. Event sourcing isn't just about recording history—it's a dynamic hub shaping an app's narrative, simultaneously serving as historian, participant, and messenger, influencing its past, present, and future.

Event Sourcing typically involves an event store, a specialized database that records each event as a distinct entry. When an event is recorded in this store, a message broker is notified. The broker then disseminates the event to interested system components. This distribution can be facilitated using Change Data Capture (CDC) mechanisms and message brokers like Kafka to stream events throughout the system. Common messaging architectures used in this context include the Publisher/Subscriber model and Queues.

Using event sourcing makes it easy to use the event-driven architecture. Event-driven architecture is used in ecosystems that have many microservices' to communicate with each other. Imagine you have 10 microservices in an e-commerce system and there is an event like (OrderPlacedEvent) and there are 5 microservices that need to know about that event, do you think using the HTTP calls will be effective? here comes the event-driven role. the order service can publish event in an event stream (e.g Kafka) and the other 5 services can subscribe to this stream and acknowledge those events from this stream.

Storing events significantly enhances our understanding of the system's activities and changes. This approach proves highly beneficial in various scenarios. Key applications of this data include debugging inconsistencies and providing users with a transparent view of how data has evolved over time, akin to the detailed history presented in a bank account statement.

A key benefit of this is in the scenarios where one wants to migrate some aspects of the application behind the scenes, say the database needs to be changed, or you are adding features which need re-architecting certain aspects. So, event sourcing is great mechanism to these changes live without downtime for users such that they do not notice any disruption to the functions they are using but allows you to manage the complexity behind the scenes since your app state is being synced across via event sourcing.

Event Sourcing offers several benefits, including improved performance. This is achieved by eliminating the need for complex WHERE clauses and record locking in database operations, thereby simplifying transactions. The pattern relies on an append-only database to store immutable events, which allows for easy tracking of user activity history and event replication when needed. It also promotes system decoupling and enhances extensibility, as new components can be seamlessly integrated by subscribing to relevant events. Moreover, managing events as simple objects simplifies both implementation and maintenance.

It's important to acknowledge that event sourcing involves storing a significantly larger volume of data compared to traditional storage methods, and it also adds complexity to data analysis. As with any technological decision, it's crucial to weigh whether the introduction of this additional complexity will yield sufficient business value to justify the extra effort.

One of the key things construct of event sourcing is the event stream reliability. Choosing the right structure of the event object, the streaming and queuing layer, and selecting the underlying optimal database is very important. On its own eventsourcing does can be implemented using existing stack like mq, kafka, cosmos db etc.

Event Sourcing enables the implementation of event-driven architectures, which require managing event communication through choreography or orchestration. While it can be implemented in both on-premises and cloud architectures, cloud environments often simplify the process. This is due to the availability of various Software-as-a-Service (SaaS), Platform-as-a-Service (PaaS), and Infrastructure-as-Code (IaC) solutions that aid in automation and replication. Additionally, it's common to pair Event Sourcing with the Command Query Responsibility Segregation (CQRS) pattern. This combination helps separate concerns and address the complexities of queries in such architectures by creating a model optimized for easier query handling.