Apache Flume vs Filebeat

Apache Flume

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Filebeat

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Apache Flume vs Filebeat: What are the differences?

What is Apache Flume? A service for collecting, aggregating, and moving large amounts of log data. It 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.

What is Filebeat? A lightweight shipper for forwarding and centralizing log data. It helps you keep the simple things simple by offering a lightweight way to forward and centralize logs and files.

Apache Flume and Filebeat can be primarily classified as "Log Management" tools.

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What is Apache Flume?

It 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.

What is Filebeat?

It helps you keep the simple things simple by offering a lightweight way to forward and centralize logs and files.
What companies use Apache Flume?
What companies use Filebeat?

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What tools integrate with Apache Flume?
What tools integrate with Filebeat?
    No integrations found
    What are some alternatives to Apache Flume and Filebeat?
    Apache Spark
    Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
    Logstash
    Logstash is a tool for managing events and logs. You can use it to collect logs, parse them, and store them for later use (like, for searching). If you store them in Elasticsearch, you can view and analyze them with Kibana.
    Apache Storm
    Apache Storm is a free and open source distributed realtime computation system. Storm makes it easy to reliably process unbounded streams of data, doing for realtime processing what Hadoop did for batch processing. Storm has many use cases: realtime analytics, online machine learning, continuous computation, distributed RPC, ETL, and more. Storm is fast: a benchmark clocked it at over a million tuples processed per second per node. It is scalable, fault-tolerant, guarantees your data will be processed, and is easy to set up and operate.
    Kafka
    Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.
    Apache Flink
    Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.
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