Project Name
Data Replication Between RDBMS Using Kafka
Overview
Our client was a prominent player in the finance industry. They required a robust solution for replicating critical financial data from their production PostgreSQL database to multiple destination databases. As the client belonged to the finance sector, it was imperative to ensure that the replicated data was both up-to-date and accurate. This approach supported real-time analytics without impacting the production system’s performance. The client aimed to provide their analytics team with the most current data while keeping the production environment secure and isolated.
Challenges
Our client was facing several challenges, including:
- Real-time Data Replication: The client needed support to replicate data from one relational database to others in real-time. It was crucial to keep the destination databases updated without affecting the performance of the production/ source database.
- Isolation of Source Database: It was crucial to prevent the analytics team or other non-operational teams from accessing the production or source database directly, to avoid potential disruptions or performance issues.
- Cost-Effective Solution: The client sought a cost-effective alternative to expensive data replication tools, opting to leverage open-source technologies for data replication.
- Minimal Latency: Ensuring minimal latency in the replication process was essential so the analytics team could work with the most current data without significant delays.
Our Solution
The Ksolves team delivered a robust solution by using the following approach: We implemented Debezium, an open-source Kafka connector, to extract data from the source database and stream it to multi-node Kafka brokers. The JDBC sink connector was then used to transfer data from Kafka to the destination databases.
- Source Database Operations: Debezium’s source connector captures real-time changes in the source database whenever operations such as inserts, updates, or deletes occur.
- Kafka Integration: The captured changes are transmitted to Apache Kafka, where they are published as messages to designated Kafka topics.
- Data Transportation: These Kafka topics hold the data changes and ensure they are available for consumption in a reliable and scalable manner.
- Sink Connector Activation: Debezium’s JDBC sink connector, configured to listen to the relevant Kafka topics, consumes the messages. This setup supports various relational database connectors.
- Data Insertion into Destination Database: The sink connector applies these data changes to the destination database in real time, ensuring that the database reflects the current state of the source database.
- Monitor Data Flow: We employed tools like Prometheus, Grafana, and the Open-Source Kafka UI to monitor the data flow through Kafka and ensure smooth operation.
Data Flow Diagram
Conclusion
We accomplished the project by enabling real-time data replication between various relational databases using Debezium connectors and Kafka. Our solution effectively addressed key challenges such as ensuring real-time data synchronization, maintaining database isolation, providing high availability and disaster recovery, and implementing a cost-effective solution. By optimizing performance and minimizing latency, we created a reliable data replication pipeline that supports seamless data integration and up-to-date analytics, meeting the client’s needs efficiently.
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