Databus is a distributed change data capture (CDC) system designed for reliably capturing and processing data changes from various data sources. It addresses the need for a consistent and scalable data pipeline to move data from primary data stores (OLTP) to secondary stores like data warehouses, search indexes, and caching layers. Databus operates by mining database transaction logs, ensuring consistency and decoupling from specific database technologies. It supports low latency and high throughput, making it suitable for real-time data processing.
Databus offers a consistent and reliable mechanism for capturing data changes, decoupling data processing from the original data source. Its architecture ensures low latency and high throughput, suitable for demanding data pipelines. The solution's source-agnostic approach prevents vendor lock-in. Databus is actively used at LinkedIn for critical data infrastructure components like the Social Graph Index.
- Source Agnostic: Supports diverse data sources like relational databases, NoSQL stores, and caching engines.
- Low Latency: Processes changes with latencies in the low milliseconds range.
- High Throughput: Handles thousands of events per second per server.
- Infinite Look-Back: Maintains a history of data changes for comprehensive data recovery and analysis.
- Rich Subscription: Provides flexible subscription mechanisms for different data processing needs.
- Scalable Architecture: Designed to handle increasing data volumes and processing demands.
- Mature Implementation: Used extensively in production at LinkedIn, validated for reliability and performance.
Databus is a mature and actively maintained project, with a substantial history of use at LinkedIn. The project has a stable codebase and a well-defined architecture. Regular updates and bug fixes indicate ongoing support and attention from the development team. The availability of documentation and example code suggests a well-supported ecosystem.
Databus benefits data engineers and data scientists seeking a reliable and scalable CDC solution to build real-time data pipelines. It enables consistent data synchronization across different platforms, reducing data latency and facilitating complex analytical workloads. By decoupling data processing from the source systems, Databus offers flexibility and mitigates vendor lock-in, enhancing the overall agility and value of data infrastructure.