Alternatives to AWS CloudTrail logo

Alternatives to AWS CloudTrail

AWS Config, AWS X-Ray, Splunk, Logstash, and Papertrail are the most popular alternatives and competitors to AWS CloudTrail.
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What is AWS CloudTrail and what are its top alternatives?

With CloudTrail, you can get a history of AWS API calls for your account, including API calls made via the AWS Management Console, AWS SDKs, command line tools, and higher-level AWS services (such as AWS CloudFormation). The AWS API call history produced by CloudTrail enables security analysis, resource change tracking, and compliance auditing. The recorded information includes the identity of the API caller, the time of the API call, the source IP address of the API caller, the request parameters, and the response elements returned by the AWS service.
AWS CloudTrail is a tool in the Log Management category of a tech stack.

Top Alternatives of AWS CloudTrail

AWS CloudTrail alternatives & related posts

AWS Config logo

AWS Config

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Config gives you a detailed inventory of your AWS resources and their current configuration, and continuously records configuration...
AWS Config logo
AWS Config
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AWS CloudTrail logo
AWS CloudTrail
AWS X-Ray logo

AWS X-Ray

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An application performance management service
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    AWS X-Ray logo
    AWS X-Ray
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    AWS CloudTrail logo
    AWS CloudTrail
    Splunk logo

    Splunk

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    Search, monitor, analyze and visualize machine data
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      Splunk logo
      Splunk
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      AWS CloudTrail logo
      AWS CloudTrail

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      Kibana
      Kibana
      Splunk
      Splunk
      Grafana
      Grafana

      I use Kibana because it ships with the ELK stack. I don't find it as powerful as Splunk however it is light years above grepping through log files. We previously used Grafana but found it to be annoying to maintain a separate tool outside of the ELK stack. We were able to get everything we needed from Kibana.

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      Logstash logo

      Logstash

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      Collect, Parse, & Enrich Data
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      Tymoteusz Paul
      Tymoteusz Paul
      Devops guy at X20X Development LTD | 21 upvotes 1.7M views
      Vagrant
      Vagrant
      VirtualBox
      VirtualBox
      Ansible
      Ansible
      Elasticsearch
      Elasticsearch
      Kibana
      Kibana
      Logstash
      Logstash
      TeamCity
      TeamCity
      Jenkins
      Jenkins
      Slack
      Slack
      Apache Maven
      Apache Maven
      Vault
      Vault
      Git
      Git
      Docker
      Docker
      CircleCI
      CircleCI
      LXC
      LXC
      Amazon EC2
      Amazon EC2

      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
      Tanya Bragin
      Product Lead, Observability at Elastic | 10 upvotes 371.9K views
      atElasticElastic
      Elasticsearch
      Elasticsearch
      Logstash
      Logstash
      Kibana
      Kibana

      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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      related Papertrail posts

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      #Heroku

      Logentries, LogDNA, Timber.io, Papertrail and Sumo Logic provide free pricing plan for #Heroku application. You can add these applications as add-ons very easily.

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      ELK logo

      ELK

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      The acronym for three open source projects: Elasticsearch, Logstash, and Kibana
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        ELK logo
        ELK
        VS
        AWS CloudTrail logo
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        Wallace Alves
        Wallace Alves
        Cyber Security Analyst | 1 upvotes 240.4K views
        Docker
        Docker
        Docker Compose
        Docker Compose
        Portainer
        Portainer
        ELK
        ELK
        Elasticsearch
        Elasticsearch
        Kibana
        Kibana
        Logstash
        Logstash
        nginx
        nginx

        Docker Docker Compose Portainer ELK Elasticsearch Kibana Logstash nginx

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        Fluentd logo

        Fluentd

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        Unified logging layer
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        Fluentd
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        AWS CloudTrail