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  1. Stackups
  2. DevOps
  3. Monitoring
  4. Network Monitoring
  5. Filebeat vs Packetbeat

Filebeat vs Packetbeat

OverviewComparisonAlternatives

Overview

Packetbeat
Packetbeat
Stacks15
Followers44
Votes4
Filebeat
Filebeat
Stacks133
Followers252
Votes0

Filebeat vs Packetbeat: What are the differences?

Introduction

In this Markdown code, we will discuss the key differences between Filebeat and Packetbeat, two popular components of the Elastic Stack used for data collection and analysis.

  1. Data Type: Filebeat is primarily used for shipping log files, while Packetbeat is designed for the collection and analysis of network packet data.

  2. Data Source: Filebeat monitors log files and directories on the server, tailing and shipping the log events to the defined output, which can be a centralized log management system. Packetbeat, on the other hand, captures network traffic and analyzes it to provide insights into application behavior and performance.

  3. Protocol Analysis: While Filebeat focuses on file-based data, Packetbeat performs protocol analysis by capturing network packets and analyzing the protocol-specific information, such as HTTP requests, DNS queries, or MySQL queries.

  4. Layer of Operation: Filebeat operates at the file system layer, monitoring specific files or directories for changes and shipping the data. Packetbeat operates at the transport layer, capturing packets from the network interface, and analyzing various network protocols.

  5. Use Cases: Filebeat is commonly used for log file centralization and shipping, providing real-time log data to a centralized location for further processing and analysis. Packetbeat, on the other hand, is utilized for network monitoring, troubleshooting, and performance analysis of specific applications or services.

  6. Deployment Scenario: Filebeat is typically deployed on servers or hosts where log files need to be shipped. Packetbeat is often used in distributed environments, capturing network traffic from multiple hosts or network devices for comprehensive analysis.

In summary, Filebeat is focused on log file collection and shipping, while Packetbeat is designed for network packet analysis. They differ in terms of data type, data source, protocol analysis, layer of operation, specific use cases, and deployment scenarios.

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Detailed Comparison

Packetbeat
Packetbeat
Filebeat
Filebeat

Packetbeat agents sniff the traffic between your application processes, parse on the fly protocols like HTTP, MySQL, Postgresql or REDIS and correlate the messages into transactions.

It helps you keep the simple things simple by offering a lightweight way to forward and centralize logs and files.

Packetbeat Statistics: Contains high-level views like the network topology, the application layer protocols repartition, the response times repartition, and others;Packetbeat Search: This page enables you to do full text searches over the indexed network messages;Packetbeat Query Analysis: This page demonstrates more advanced statistics like the top N slow SQL queries, the database throughput or the most common MySQL erro
-
Statistics
Stacks
15
Stacks
133
Followers
44
Followers
252
Votes
4
Votes
0
Pros & Cons
Pros
  • 2
    Easy setup
  • 2
    Works well with ELK stack
No community feedback yet
Integrations
No integrations available
Logstash
Logstash

What are some alternatives to Packetbeat, Filebeat?

Papertrail

Papertrail

Papertrail helps detect, resolve, and avoid infrastructure problems using log messages. Papertrail's practicality comes from our own experience as sysadmins, developers, and entrepreneurs.

Logmatic

Logmatic

Get a clear overview of what is happening across your distributed environments, and spot the needle in the haystack in no time. Build dynamic analyses and identify improvements for your software, your user experience and your business.

Loggly

Loggly

It is a SaaS solution to manage your log data. There is nothing to install and updates are automatically applied to your Loggly subdomain.

Logentries

Logentries

Logentries makes machine-generated log data easily accessible to IT operations, development, and business analysis teams of all sizes. With the broadest platform support and an open API, Logentries brings the value of log-level data to any system, to any team member, and to a community of more than 25,000 worldwide users.

Logstash

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.

Graylog

Graylog

Centralize and aggregate all your log files for 100% visibility. Use our powerful query language to search through terabytes of log data to discover and analyze important information.

Sematext

Sematext

Sematext pulls together performance monitoring, logs, user experience and synthetic monitoring that tools organizations need to troubleshoot performance issues faster.

Fluentd

Fluentd

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.

ELK

ELK

It is the acronym for three open source projects: Elasticsearch, Logstash, and Kibana. Elasticsearch is a search and analytics engine. Logstash is a server‑side data processing pipeline that ingests data from multiple sources simultaneously, transforms it, and then sends it to a "stash" like Elasticsearch. Kibana lets users visualize data with charts and graphs in Elasticsearch.

Sumo Logic

Sumo Logic

Cloud-based machine data analytics platform that enables companies to proactively identify availability and performance issues in their infrastructure, improve their security posture and enhance application rollouts. Companies using Sumo Logic reduce their mean-time-to-resolution by 50% and can save hundreds of thousands of dollars, annually. Customers include Netflix, Medallia, Orange, and GoGo Inflight.

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