Cloud Computing, Data Analytics

4 Mins Read

Choosing the Right Tool for Log Data Management: Filebeat vs Fluentd


Log data management is an essential aspect of any modern organization. With the increasing amount of data generated from different sources, it’s crucial to have the right tool to collect, process, and analyze log data. Two popular open-source tools for log data management are Filebeat and Fluentd.

We’ll compare the features, performance, and use cases of Filebeat and Fluentd to help you choose the right tool for your log data management needs. Whether you’re a small business or a large enterprise, understanding the differences between Filebeat and Fluentd will help you decide which tool to use for your log data management. So, let’s compare these two popular log shipping tools.

Architecture diagram of Filebeat


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Architecture diagram of Fluentd


What is Filebeat?

Filebeat is a lightweight, open-source data shipping tool developed by Elastic. It is designed to collect log data from various sources, such as files, syslog, and other third-party systems, and send it to a configured output destination, such as Elasticsearch, Logstash, or Kafka. Filebeat is part of the Elastic Stack, a collection of open-source logging, monitoring, and analytics tools.

Filebeat is written in Go and uses a lightweight libbeat framework to ship logs efficiently. It reads log files line by line, and by default, it starts reading from the end of a file and sends data to an output destination in near real-time. File beat supports different data formats, including JSON, XML, and CSV. It also includes pre-built modules for collecting system metrics, such as CPU usage, memory usage, disk IO, and network IO.

Organizations of all sizes widely use Filebeat to collect and send log data to a centralized location, which can be analyzed and monitored for anomalies or other insights. It’s easy to set up and configure, and its lightweight design doesn’t consume too many system resources.

What is Fluentd?

Fluentd is an open-source, scalable log data collector developed by Treasure Data. It’s designed to unify logging infrastructure by collecting data from various sources, processing it, and forwarding it to different output destinations, such as Elasticsearch, Fluent Bit, etc. Fluentd can collect data from various sources, including files, syslog, TCP/UDP, and other third-party systems.

Fluentd is written in Ruby and has a plugin-based architecture that allows users to extend its functionality with custom plugins. It uses a tag-based system to route log data to different output destinations and supports different data formats, including JSON, CSV, and Apache logs. Fluentd also includes a rich set of built-in plugins for data filtering, buffering, and transformation, which can be configured through a simple configuration file.

Organizations of all sizes widely use Fluentd to collect, process, and forward log data to different output destinations. Its plugin-based architecture makes it highly customizable, and its ability to handle large amounts of data makes it ideal for high-traffic environments. Fluentd’s flexibility and scalability make it a popular choice for log data management in many industries.

Comparison between Filebeat and Fluentd


Use Cases of Filebeat

Filebeat is a versatile tool that can be used for various log data collection and monitoring use cases.

  1. Log collection from files: Filebeat can collect log data from log files generated by applications or systems. It monitors the specified log files and sends the data to the output destination in near real time.
  2. System monitoring: Filebeat has pre-built modules that collect system metrics from different operating systems. These modules make it easy to monitor system performance and identify issues.
  3. Integration with other tools: Filebeat can be integrated with tools like Elasticsearch, Logstash, and Kafka. It sends log data to these tools, which can be used for further analysis or visualization.
  4. Security monitoring: Filebeat can collect security-related log data, such as login attempts, file access, and system errors. This data can be analyzed to identify security threats and vulnerabilities.
  5. Container monitoring: Filebeat can collect log data from containerized applications on platforms like Kubernetes or Docker. This allows users to monitor the performance of containerized applications and identify issues.

Use Cases of Fluentd

Fluentd’s flexibility and scalability suit many log data management use cases.

  1. Collecting and forwarding log data: Fluentd can collect log data from various sources, including files, Syslog, TCP/UDP, and third-party systems. It can then forward this data to different output destinations, such as Elasticsearch, Fluent Bit, Amazon S3, Hadoop, MongoDB, etc.
  2. Transforming and filtering data: Fluentd’s plugin-based architecture allows users to customize data processing and filtering. Users can create custom plugins to transform data into different formats or filter out unwanted data.
  3. Centralized logging: Fluentd can aggregate log data from multiple sources into a centralized log repository. This can help organizations track system and application performance, troubleshoot issues, and identify potential security threats.
  4. Real-time data analytics: Fluentd can stream log data to data analytics platforms like Apache Spark or Hadoop, allowing organizations to analyze and visualize data in real time.
  5. Cloud monitoring: Fluentd can collect and forward log data from cloud-based services like AWS or GCP. This can help organizations monitor cloud-based applications and infrastructure, identify performance issues, and optimize resources.
  6. Internet of Things (IoT) data management: Fluentd can handle large volumes of data from IoT devices, making it ideal for managing and analyzing IoT data. Fluentd’s ability to filter and process real-time data can help organizations identify and respond to critical IoT events.


Filebeat is a lightweight tool that is easy to set up and use. It’s ideal for collecting log data from files and sending it to an output destination in near real-time. On the other hand, Fluentd is a more scalable tool that can handle large amounts of log data from different sources.

In summary, Filebeat is a good choice for small to medium-sized organizations that need a lightweight tool for collecting and shipping log data from files. Fluentd is a better choice for larger organizations or high-traffic environments that need a more scalable and customizable tool for log data management.

Ultimately, the choice between Filebeat and Fluentd depends on your organization’s specific needs and use cases. By understanding the strengths and weaknesses of each tool, you can make an informed decision and choose the right tool for your log data management needs.

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Drop a query if you have any questions regarding Filebeat, Fluentd and I will get back to you quickly.

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1. Can Filebeat run on Windows?

ANS: – Yes, File beat can run on Windows, Linux, macOS, and other operating systems.

2. Can I customize Filebeat's output destination?

ANS: – Yes, you can configure Filebeat to send data to Elasticsearch, Logstash, Kafka, or any other output destination that supports the Beats input.

3. What languages is Fluentd written in?

ANS: – Fluentd is written in Ruby, and it has a plugin-based architecture that allows users to extend its functionality with custom plugins.

4. What output destinations does Fluentd support?

ANS: – Fluentd supports various output destinations, including Elasticsearch, Fluent Bit, Amazon S3, Hadoop, MongoDB, and more.

WRITTEN BY Ramyashree V

Ramyashree V is working as a Research Associate in CloudThat. She is an expert in Kubernetes and works on many containerization-based solutions for clients. She is interested in learning new technologies in Cloud services.



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