Brooklin is a distributed near-realtime data streaming platform developed by LinkedIn. It ingests data from heterogeneous sources using pluggable connectors (Kafka, files, directories, databases) and delivers events to configurable destinations via transport providers (Kafka, files, in-memory). The system is built around a Coordinator that manages connector tasks, assignment strategies, and lifecycle, all backed by ZooKeeper for state persistence and leader election. A REST.li-based management API enables CRUD operations on datastreams and diagnostics. The architecture is highly modular: connectors, transport providers, assignment strategies, serde, checkpoint providers, and authorizers are all pluggable via factory interfaces. Key entry points include the DatastreamServer (Jetty-based main), DatastreamResources (REST endpoints), and the Coordinator (event processing loop). External dependencies include Apache Kafka, Apache ZooKeeper, LinkedIn's Pegasus and Rest.li frameworks, Jetty, Netty, and ParSeq for asynchronous operations. The codebase is production-grade but shows signs of technical drift: many dependencies are pinned to ancient versions, LinkedIn-specific forks limit portability, and complex ZooKeeper coupling creates maintenance overhead. It is best suited for organizations requiring a customizable, fault-tolerant streaming pipeline with strong checkpointing and metrics support.