• Real-time data streaming: Kafka, as a distributed messaging queue system, provides high throughput and low latency for real-time data processing. Streaming Oracle CDC events to Kafka facilitates real-time capture and processing of data, enabling businesses to rapidly respond to data changes.
• High availability: Kafka ensures high availability through distribution. A Kafka cluster usually consists of multiple brokers, each responsible for storing a segment of the data with replication. Thus, even in the event of a broker failure, other brokers can continue operating, ensuring service availability.
• Scalability: Kafka’s distributed architecture, coupled with features such as consumer groups, partitioning strategy, replication mechanism, horizontal scalability, high concurrency processing capabilities, and fault tolerance, enables it to handle massive message data, meeting demands for high throughput and concurrency.
• Efficient querying: Leveraging techniques like sequential write, indexing, binary search, and in-memory caching, Kafka efficiently processes massive message streams while maintaining high performance and low latency for efficient data retrieval.
• High-concurrency writes: Kafka emphasizes high-concurrency data writes and employs various technologies such as zero-copy, batch processing, message compression, and asynchronous processing to enhance data transfer efficiency and processing speed. This capability enables Kafka to handle large volumes of data streams effectively, making it suitable for high-throughput scenarios such as log recording and event sourcing.
• Decoupling data producers and consumers: Kafka’s message queue model facilitates decoupling between data producers and consumers, allowing database changes to form a loose coupling with actual data consumers such as applications and analytics systems, thereby enhancing system flexibility.
• Support for event-driven architecture: Synchronizing Oracle data to Kafka enables the construction of event-driven architectures. Database changes can be transmitted as event streams, triggering actions in other components of the system and enabling more flexible and agile business processes.
• Data Integration: As middleware, Kafka can coordinate data flows between different systems, facilitating seamless integration with other data sources and targets. This capability enables systems to better adapt to complex data processing and exchange requirements.

Why real-time data sync matters?

• Real-time reporting and monitoring systems: Businesses rely on real-time monitoring and reporting for operational insights. This includes operational and performance monitoring, where access to database changes in real-time is paramount. By synchronizing data to Kafka in real-time, monitoring systems remain current and effective.
• Event-driven architectures: Modern applications often adopt event-driven architectures, facilitating system integration through a publish-subscribe model. Real-time data synchronization to Kafka is vital for ensuring timely event propagation and processing.
• Enhanced user experience: Applications requiring real-time interaction and responsiveness demand up-to-date data presentation. For instance, in online collaboration or real-time communication apps, users expect immediate visibility into others’ actions and changes.

By now, we have gained a basic understanding of the importance of real-time synchronization of data from Oracle to Kafka. Next, let’s explore some common synchronization methods.