Which events you really need to back up

Most event hubs are resilient and able to recover from failures, but they do not offer a full-fledged backup solution. Take Apache Kafka, for example, one of the most popular platforms for real-time data streaming, processing, and event-driven architectures. If your mission-critical systems rely on this technology, then having a backup strategy is essential.

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In this blog, we use Kafka to illustrate which data is at risk, though everything discussed is equally applicable to other event hub platforms, such as Redpanda or Azure Event Hub.

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The following four events require this backup strategy:

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Events with business-critical information

Events that carry valuable data that can influence key business decisions, streamline operations, and elevate the customer experience. The primary reasons to preserve such events include:

• Real-time decision-making: storing events enables businesses to make timely, data-driven decisions, whether for personalization, operational improvements, or fraud detection.
• Supporting critical applications: events that power essential processes—such as order processing, payments, or inventory management—must be retained to ensure smooth business operations.
• Auditing and regulatory compliance: in many regulated industries, it’s mandatory to maintain records of certain transactions or events to meet legal and compliance requirements. Events stored in Kafka provide a detailed log of business activities and transactions, essential for auditing. Industries like finance and healthcare often need to adhere to regulations like GDPR, HIPAA, or financial reporting standards.
• Enhancing machine learning models: retaining historical and real-time event data is essential for training and fine-tuning machine learning models. Models improve over time with access to more data, and past events help refine them. Real-time data can also be used to integrate feedback loops into models, improving accuracy in areas such as fraud detection and recommendation systems.
• Historical analysis and insights: events often capture vital transactional and operational data that can be analyzed for historical trends, performance evaluations, or forecasting.

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Events used for sourcing

With event sourcing, the current state of an entity (such as a customer account or an order) isn’t stored directly. Instead, the state is rebuilt by replaying every event associated with that entity, from the first event to the most recent. If even a single event is missing, the resulting state will be incomplete or incorrect.

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Many business processes, like calculating totals or reversing prior actions (through compensating events), require the complete set of events. Missing events can cause the business logic to execute incorrectly, resulting in faulty outcomes or the failure to properly apply compensations. In the event of a system failure, event sourcing depends on replaying all past events to restore the entity’s state. If any events are missing, the system won’t be able to fully recover, leading to data loss or inconsistencies.

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Events for stateful processing

Stream processing relies on state stores (such as Kafka Streams with RocksDB) to handle and store the local state for each partition. These stores need the complete set of events to compute accurate states over time. Without access to the full event history, these stores would hold incomplete or incorrect data, causing errors in downstream processing.

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Stateful operations, like joins and aggregations, depend on having all relevant events to maintain accurate state. In a join, events from two streams are merged based on specific conditions (such as a common key or time window). If events are missing from either stream, the join will yield incomplete or incorrect results. For aggregations (such as sums, averages, or counts), even a single missing event can skew the final results.Many stateful streaming operations in Kafka use time windows (such as sliding or tumbling windows) to group and process events. If some events are missing from the window, the aggregation or computation will be incomplete, leading to inaccurate results. For instance, if you're calculating the total number of purchases in an hour, missing events would result in an incorrect total for that period.

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Events you may need to reprocess

Reprocessing events is vital in event-driven architectures for several reasons:

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• Error recovery and fault tolerance: in the case of a system failure, reprocessing events allows the system to restore its state by replaying events from a specific point. This ensures the system can return to a consistent state without data loss. If an issue arises (such as a coding error or misconfiguration), reprocessing events with corrected logic allows the system to fix the error and reapply business rules effectively.
• Adapting to evolving business logic: as business rules and regulations change (e.g., new tax laws or pricing strategies), reprocessing allows past events to be updated based on the latest business logic. This helps keep the system's state aligned with current rules and enables the triggering of new business processes or generating fresh insights. Reprocessing is also essential when the data model or schema evolves, ensuring older events are compatible with new formats.
• Debugging and troubleshooting: reprocessing makes it possible to replay sequences of events that may have led to an error or bug, making it easier for developers to diagnose and resolve issues. By replaying the event stream, teams can better reproduce problems and confirm that their fixes are working as intended.
• Supporting new services or features: When introducing a new service or feature, reprocessing existing events provides the historical data needed for the service to function correctly, eliminating the need to gather new data from scratch. Similarly, reprocessing is useful when migrating data to a new system, as it helps populate the new system with necessary historical data.

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Conclusion

‍Kafka manages numerous types of events that necessitate long-term retention. Implementing a robust backup strategy helps organizations preserve data integrity, protect against human errors, and ensure operational continuity even during unforeseen failures.

If you're in a critical sector like financial services, healthcare, retail, or IoT, it’s essential to consider incorporating a Kafka backup solution, such as Kannika Armory, into your data management and disaster recovery strategy.

Are you ready to safeguard your event-driven data streams?

‍Schedule a demo or start a free trial today to experience the full benefits of Kannika Armory.