Flume is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data. It has a simple and flexible architecture based on streaming data flows. It is robust and fault tolerant with tunable reliability mechanisms and many failover and recovery mechanisms. It uses a simple extensible data model that allows for online analytic application.

Flume - Log management using HDFS

Fluentd collects events from various data sources and writes them to files, RDBMS, NoSQL, IaaS, SaaS, Hadoop and so on. Fluentd helps you unify your logging infrastructure. Fluentd can be used to tail access/error logs and transport them reliably to remote systems. It can "grep" for events and send out alerts. It can function as middleware to enable asynchronous, scalable logging for user action events.
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Fluentd tries to structure data as JSON as much as possible: this allows Fluentd to unify all facets of processing log data: collecting, filtering, buffering, and outputting logs across multiple sources and destinations (Unified Logging Layer). The downstream data processing is much easier with JSON, since it has enough structure to be accessible while retaining flexible schemas.

Fluentd collects events from various data sources and writes them to files, RDBMS, NoSQL, IaaS, SaaS, Hadoop and so on. Fluentd helps you unify your logging infrastructure. Fluentd can be used to tail access/error logs and transport them reliably to remote systems. It can "grep" for events and send out alerts. It can function as middleware to enable asynchronous, scalable logging for user action events.

Fluentd - Unified Logging Layer

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Fluentd - Unified Logging Layer

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Flume - Log management using HDFS

Fluentd - Unified Logging Layer

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