Alternatives to Beats logo

Alternatives to Beats

Logstash, Wireshark, PRTG, LibreNMS, and Riemann are the most popular alternatives and competitors to Beats.
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What is Beats and what are its top alternatives?

Beats is the platform for single-purpose data shippers. They send data from hundreds or thousands of machines and systems to Logstash or Elasticsearch.
Beats is a tool in the Network Monitoring category of a tech stack.

Top Alternatives to Beats

  • 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. ...

  • Wireshark

    Wireshark

    It is the world鈥檚 foremost and widely-used network protocol analyzer. It lets you see what鈥檚 happening on your network at a microscopic level and is the de facto standard across many commercial and non-profit enterprises, government agencies, and educational institutions. ...

  • PRTG

    PRTG

    It can monitor and classify system conditions like bandwidth usage or uptime and collect statistics from miscellaneous hosts as switches, routers, servers and other devices and applications. ...

  • LibreNMS

    LibreNMS

    It is an auto-discovering PHP/MySQL/SNMP based network monitoring which includes support for a wide range of network hardware and operating systems including Cisco, Linux, FreeBSD, Juniper, Brocade, Foundry, HP and many more. ...

  • Riemann

    Riemann

    Riemann aggregates events from your servers and applications with a powerful stream processing language. Send an email for every exception in your app. Track the latency distribution of your web app. See the top processes on any host, by memory and CPU. ...

  • Nagios XI

    Nagios XI

    It is the most powerful and trusted network monitoring software on the market. It extends on proven, enterprise-class Open Source components to deliver the best network, server and application monitoring solution for today's demanding organizational requirements. ...

  • Snort

    Snort

    It is an open-source, free and lightweight network intrusion detection system (NIDS) software for Linux and Windows to detect emerging threats. ...

  • DPDK

    DPDK

    DPDK is the Data Plane Development Kit that consists of libraries to accelerate packet processing workloads running on a wide variety of CPU architectures. ...

Beats alternatives & related posts

Logstash logo

Logstash

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Collect, Parse, & Enrich Data
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PROS OF LOGSTASH
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    Free
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    Easy but powerful filtering
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    Scalable
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    Kibana provides machine learning based analytics to log
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    Great to meet GDPR goals
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    Well Documented
CONS OF LOGSTASH
  • 3
    Memory-intensive
  • 1
    Documentation difficult to use

related Logstash posts

Tymoteusz Paul
Devops guy at X20X Development LTD | 21 upvotes 路 4.3M views

Often enough I have to explain my way of going about setting up a CI/CD pipeline with multiple deployment platforms. Since I am a bit tired of yapping the same every single time, I've decided to write it up and share with the world this way, and send people to read it instead ;). I will explain it on "live-example" of how the Rome got built, basing that current methodology exists only of readme.md and wishes of good luck (as it usually is ;)).

It always starts with an app, whatever it may be and reading the readmes available while Vagrant and VirtualBox is installing and updating. Following that is the first hurdle to go over - convert all the instruction/scripts into Ansible playbook(s), and only stopping when doing a clear vagrant up or vagrant reload we will have a fully working environment. As our Vagrant environment is now functional, it's time to break it! This is the moment to look for how things can be done better (too rigid/too lose versioning? Sloppy environment setup?) and replace them with the right way to do stuff, one that won't bite us in the backside. This is the point, and the best opportunity, to upcycle the existing way of doing dev environment to produce a proper, production-grade product.

I should probably digress here for a moment and explain why. I firmly believe that the way you deploy production is the same way you should deploy develop, shy of few debugging-friendly setting. This way you avoid the discrepancy between how production work vs how development works, which almost always causes major pains in the back of the neck, and with use of proper tools should mean no more work for the developers. That's why we start with Vagrant as developer boxes should be as easy as vagrant up, but the meat of our product lies in Ansible which will do meat of the work and can be applied to almost anything: AWS, bare metal, docker, LXC, in open net, behind vpn - you name it.

We must also give proper consideration to monitoring and logging hoovering at this point. My generic answer here is to grab Elasticsearch, Kibana, and Logstash. While for different use cases there may be better solutions, this one is well battle-tested, performs reasonably and is very easy to scale both vertically (within some limits) and horizontally. Logstash rules are easy to write and are well supported in maintenance through Ansible, which as I've mentioned earlier, are at the very core of things, and creating triggers/reports and alerts based on Elastic and Kibana is generally a breeze, including some quite complex aggregations.

If we are happy with the state of the Ansible it's time to move on and put all those roles and playbooks to work. Namely, we need something to manage our CI/CD pipelines. For me, the choice is obvious: TeamCity. It's modern, robust and unlike most of the light-weight alternatives, it's transparent. What I mean by that is that it doesn't tell you how to do things, doesn't limit your ways to deploy, or test, or package for that matter. Instead, it provides a developer-friendly and rich playground for your pipelines. You can do most the same with Jenkins, but it has a quite dated look and feel to it, while also missing some key functionality that must be brought in via plugins (like quality REST API which comes built-in with TeamCity). It also comes with all the common-handy plugins like Slack or Apache Maven integration.

The exact flow between CI and CD varies too greatly from one application to another to describe, so I will outline a few rules that guide me in it: 1. Make build steps as small as possible. This way when something breaks, we know exactly where, without needing to dig and root around. 2. All security credentials besides development environment must be sources from individual Vault instances. Keys to those containers should exist only on the CI/CD box and accessible by a few people (the less the better). This is pretty self-explanatory, as anything besides dev may contain sensitive data and, at times, be public-facing. Because of that appropriate security must be present. TeamCity shines in this department with excellent secrets-management. 3. Every part of the build chain shall consume and produce artifacts. If it creates nothing, it likely shouldn't be its own build. This way if any issue shows up with any environment or version, all developer has to do it is grab appropriate artifacts to reproduce the issue locally. 4. Deployment builds should be directly tied to specific Git branches/tags. This enables much easier tracking of what caused an issue, including automated identifying and tagging the author (nothing like automated regression testing!).

Speaking of deployments, I generally try to keep it simple but also with a close eye on the wallet. Because of that, I am more than happy with AWS or another cloud provider, but also constantly peeking at the loads and do we get the value of what we are paying for. Often enough the pattern of use is not constantly erratic, but rather has a firm baseline which could be migrated away from the cloud and into bare metal boxes. That is another part where this approach strongly triumphs over the common Docker and CircleCI setup, where you are very much tied in to use cloud providers and getting out is expensive. Here to embrace bare-metal hosting all you need is a help of some container-based self-hosting software, my personal preference is with Proxmox and LXC. Following that all you must write are ansible scripts to manage hardware of Proxmox, similar way as you do for Amazon EC2 (ansible supports both greatly) and you are good to go. One does not exclude another, quite the opposite, as they can live in great synergy and cut your costs dramatically (the heavier your base load, the bigger the savings) while providing production-grade resiliency.

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Tanya Bragin
Product Lead, Observability at Elastic | 10 upvotes 路 589.2K views

ELK Stack (Elasticsearch, Logstash, Kibana) is widely known as the de facto way to centralize logs from operational systems. The assumption is that Elasticsearch (a "search engine") is a good place to put text-based logs for the purposes of free-text search. And indeed, simply searching text-based logs for the word "error" or filtering logs based on a set of a well-known tags is extremely powerful, and is often where most users start.

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Wireshark logo

Wireshark

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A free and open-source protocol analyzer
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      PRTG logo

      PRTG

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      A powerful & easy network monitoring software
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          LibreNMS logo

          LibreNMS

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          Opensource Auto-discoverying network monitoring system
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              Riemann logo

              Riemann

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              A network monitoring system
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              PROS OF RIEMANN
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                Sophisticated stream processing DSL
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                Clojure-based stream processing
              CONS OF RIEMANN
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                Nagios XI logo

                Nagios XI

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                Enterprise Server and Network Monitoring Software
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                    Snort logo

                    Snort

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                    An open-source security software product that looks at network traffic in real time and logs packets to perform...
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                        DPDK logo

                        DPDK

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                        he Data Plane Development Kit consists of libraries to accelerate packet processing workloads
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