We build world class pub-sub messaging, data capture/data ingestion and event/data processing infrastructure that allows developers at LinkedIn to easily build and operate applications.


Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design. Kafka topics can be partitioned for parallel consumption by multiple consumers.

Kafka is a wildly successful Apache open source project that was developed at LinkedIn and is used throughout the industry by companies including Yahoo, Twitter, Netflix, Pinterest, Uber, Airbnb and hundreds of others. LinkedIn continues to be a leading contributor to the Apache source base, as well as maintaining an internal ecosystem surrounding Apache Kafka.

LinkedIn operates the largest known Kafka installation anywhere - sending approximately 2.1 trillion messages a day.


Datastream is the next generation version of Databus, LinkedIn's change-capture solution for consuming database updates. Datastream, along with Kafka (for message pub-sub), and Samza (for realtime stream-processing), form the essential pieces of the stream-processing pipeline within LinkedIn.


Apache Samza is a distributed stream processing framework. It uses Apache Kafka for messaging, and Apache Hadoop YARN to provide fault tolerance, processor isolation, security, and resource management.

  • Simple API: Unlike most low-level messaging system APIs, Samza provides a very simple callback-based “process message” API comparable to MapReduce.
  • Managed state: Samza manages snapshotting and restoration of a stream processor’s state. When the processor is restarted, Samza restores its state to a consistent snapshot. Samza is built to handle large amounts of state (many gigabytes per partition).
  • Fault tolerance: Whenever a machine in the cluster fails, Samza works with YARN to transparently migrate your tasks to another machine.
  • Durability: Samza uses Kafka to guarantee that messages are processed in the order they were written to a partition, and that no messages are ever lost.
  • Scalability: Samza is partitioned and distributed at every level. Kafka provides ordered, partitioned, replayable, fault-tolerant streams. YARN provides a distributed environment for Samza containers to run in.
  • Pluggable: Though Samza works out of the box with Kafka and YARN, Samza provides a pluggable API that lets you run Samza with other messaging systems and execution environments.
  • Processor isolation: Samza works with Apache YARN, which supports Hadoop’s security model, and resource isolation through Linux CGroups.