Alternatives to Logstash logo

Alternatives to Logstash

Fluentd, Splunk, Kafka, Beats, and Graylog are the most popular alternatives and competitors to Logstash.
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What is Logstash and what are its top alternatives?

Logstash is a widely-used open source data processing tool that allows users to collect, transform, and store data from various sources. Its key features include real-time data processing, data enrichment, and support for various data formats and protocols. However, some limitations of Logstash include its complexity in configuration, resource-intensive nature, and the need for additional tools to handle data storage and visualization.

  1. Fluentd: Fluentd is an open source data collector that allows for efficient and robust data forwarding. It features easy setup, support for various sources and destinations, and a rich plugin ecosystem. Pros include high performance and stability, while cons include potential complexity for users unfamiliar with the tool.
  2. Apache NiFi: Apache NiFi is a powerful data automation tool that enables users to automate data flow between different systems. It features a user-friendly interface, extensive data processing capabilities, and strong security features. Pros include visual data flow design, while cons include a potentially steep learning curve.
  3. Splunk: Splunk is a popular data analysis platform that offers real-time visibility into machine-generated data. It features powerful search capabilities, data visualization tools, and extensive integrations. Pros include ease of use and rich features, while cons include potential cost barriers for large deployments.
  4. Elasticsearch: Elasticsearch is a distributed, RESTful search and analytics engine that is often used in conjunction with Logstash and Kibana. It features real-time data processing, scalability, and powerful search capabilities. Pros include robust search functionality, while cons include potential complexity in deployment and maintenance.
  5. Apache Storm: Apache Storm is a distributed real-time computation system that enables users to process large streams of data in real-time. It features high scalability, fault tolerance, and support for complex event processing. Pros include low latency processing, while cons include potential complexity in configuration and setup.
  6. Vector: Vector is a lightweight and high-performance open source data pipeline tool that focuses on simplicity and ease of use. It features real-time data processing, observability tools, and extensive support for data sources and destinations. Pros include low resource usage and efficient data processing, while cons include a smaller plugin ecosystem compared to other tools.
  7. Filebeat: Filebeat is a lightweight open source data shipping tool that specializes in forwarding log files to various locations. It features easy deployment, efficient log processing, and support for various data formats. Pros include simplicity and low resource usage, while cons include limited data processing capabilities compared to other tools.
  8. Logagent: Logagent is an open source log shipper and parser that focuses on real-time log processing and analysis. It features high performance, extensibility through plugins, and easy deployment. Pros include real-time log processing and plugins for various data sources, while cons include a potentially smaller community and plugin ecosystem.
  9. Chukwa: Chukwa is an open source data collection system that is designed for scalable and reliable monitoring of large distributed systems. It features flexible data collection, data retention policies, and extensible architecture. Pros include scalability and reliability for large-scale monitoring, while cons include potential complexity in configuration and management.
  10. Kafka Connect: Kafka Connect is a tool included in the Apache Kafka ecosystem that enables users to build scalable and robust streaming data pipelines. It features a plugin-based architecture, fault tolerance, and integration with various data systems. Pros include seamless integration with Apache Kafka, while cons include potential complexity in plugin development and maintenance.

Top Alternatives to Logstash

  • 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. ...

  • Splunk
    Splunk

    It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data. ...

  • Kafka
    Kafka

    Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design. ...

  • Beats
    Beats

    Beats is the platform for single-purpose data shippers. They send data from hundreds or thousands of machines and systems to Logstash or Elasticsearch. ...

  • 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. ...

  • Elasticsearch
    Elasticsearch

    Elasticsearch is a distributed, RESTful search and analytics engine capable of storing data and searching it in near real time. Elasticsearch, Kibana, Beats and Logstash are the Elastic Stack (sometimes called the ELK Stack). ...

  • Filebeat
    Filebeat

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

  • Apache Flume
    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. ...

Logstash alternatives & related posts

Fluentd logo

Fluentd

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Unified logging layer
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PROS OF FLUENTD
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    Open-source
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    Great for Kubernetes node container log forwarding
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    Lightweight
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    Easy
CONS OF FLUENTD
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    Splunk logo

    Splunk

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    Search, monitor, analyze and visualize machine data
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    PROS OF SPLUNK
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      API for searching logs, running reports
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      Alert system based on custom query results
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      Dashboarding on any log contents
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      Custom log parsing as well as automatic parsing
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      Ability to style search results into reports
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      Query engine supports joining, aggregation, stats, etc
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      Splunk language supports string, date manip, math, etc
    • 2
      Rich GUI for searching live logs
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      Query any log as key-value pairs
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      Granular scheduling and time window support
    CONS OF SPLUNK
    • 1
      Splunk query language rich so lots to learn

    related Splunk posts

    Shared insights
    on
    SplunkSplunkDjangoDjango

    I am designing a Django application for my organization which will be used as an internal tool. The infra team said that I will not be having SSH access to the production server and I will have to log all my backend application messages to Splunk. I have no knowledge of Splunk so the following are the approaches I am considering: Approach 1: Create an hourly cron job that uploads the server log file to some Splunk storage for later analysis. - Is this possible? Approach 2: Is it possible just to stream the logs to some splunk endpoint? (If yes, I feel network usage and communication overhead will be a pain-point for my application)

    Is there any better or standard approach? Thanks in advance.

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    Shared insights
    on
    KibanaKibanaSplunkSplunkGrafanaGrafana

    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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    Kafka logo

    Kafka

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    PROS OF KAFKA
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      High-throughput
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      Distributed
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      Scalable
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      High-Performance
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      Durable
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      Publish-Subscribe
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      Simple-to-use
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      Open source
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      Written in Scala and java. Runs on JVM
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      Message broker + Streaming system
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      KSQL
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      Avro schema integration
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      Robust
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      Suport Multiple clients
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      Extremely good parallelism constructs
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      Partioned, replayable log
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      Simple publisher / multi-subscriber model
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      Fun
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      Flexible
    CONS OF KAFKA
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      Non-Java clients are second-class citizens
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      Needs Zookeeper
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      Operational difficulties
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      Terrible Packaging

    related Kafka posts

    Nick Rockwell
    SVP, Engineering at Fastly · | 46 upvotes · 3.5M views

    When I joined NYT there was already broad dissatisfaction with the LAMP (Linux Apache HTTP Server MySQL PHP) Stack and the front end framework, in particular. So, I wasn't passing judgment on it. I mean, LAMP's fine, you can do good work in LAMP. It's a little dated at this point, but it's not ... I didn't want to rip it out for its own sake, but everyone else was like, "We don't like this, it's really inflexible." And I remember from being outside the company when that was called MIT FIVE when it had launched. And been observing it from the outside, and I was like, you guys took so long to do that and you did it so carefully, and yet you're not happy with your decisions. Why is that? That was more the impetus. If we're going to do this again, how are we going to do it in a way that we're gonna get a better result?

    So we're moving quickly away from LAMP, I would say. So, right now, the new front end is React based and using Apollo. And we've been in a long, protracted, gradual rollout of the core experiences.

    React is now talking to GraphQL as a primary API. There's a Node.js back end, to the front end, which is mainly for server-side rendering, as well.

    Behind there, the main repository for the GraphQL server is a big table repository, that we call Bodega because it's a convenience store. And that reads off of a Kafka pipeline.

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    Ashish Singh
    Tech Lead, Big Data Platform at Pinterest · | 38 upvotes · 3M views

    To provide employees with the critical need of interactive querying, we’ve worked with Presto, an open-source distributed SQL query engine, over the years. Operating Presto at Pinterest’s scale has involved resolving quite a few challenges like, supporting deeply nested and huge thrift schemas, slow/ bad worker detection and remediation, auto-scaling cluster, graceful cluster shutdown and impersonation support for ldap authenticator.

    Our infrastructure is built on top of Amazon EC2 and we leverage Amazon S3 for storing our data. This separates compute and storage layers, and allows multiple compute clusters to share the S3 data.

    We have hundreds of petabytes of data and tens of thousands of Apache Hive tables. Our Presto clusters are comprised of a fleet of 450 r4.8xl EC2 instances. Presto clusters together have over 100 TBs of memory and 14K vcpu cores. Within Pinterest, we have close to more than 1,000 monthly active users (out of total 1,600+ Pinterest employees) using Presto, who run about 400K queries on these clusters per month.

    Each query submitted to Presto cluster is logged to a Kafka topic via Singer. Singer is a logging agent built at Pinterest and we talked about it in a previous post. Each query is logged when it is submitted and when it finishes. When a Presto cluster crashes, we will have query submitted events without corresponding query finished events. These events enable us to capture the effect of cluster crashes over time.

    Each Presto cluster at Pinterest has workers on a mix of dedicated AWS EC2 instances and Kubernetes pods. Kubernetes platform provides us with the capability to add and remove workers from a Presto cluster very quickly. The best-case latency on bringing up a new worker on Kubernetes is less than a minute. However, when the Kubernetes cluster itself is out of resources and needs to scale up, it can take up to ten minutes. Some other advantages of deploying on Kubernetes platform is that our Presto deployment becomes agnostic of cloud vendor, instance types, OS, etc.

    #BigData #AWS #DataScience #DataEngineering

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    Beats logo

    Beats

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    PROS OF BEATS
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      CONS OF BEATS
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        Graylog logo

        Graylog

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        Open source log management that actually works
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        PROS OF GRAYLOG
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          Open source
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          Powerfull
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          Well documented
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          Alerts
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          User authentification
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          Flexibel query and parsing language
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          Alerts and dashboards
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          User management
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          Easy query language and english parsing
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          Easy to install
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          Manage users and permissions
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          A large community
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          Free Version
        CONS OF GRAYLOG
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          Does not handle frozen indices at all

        related Graylog posts

        Elasticsearch logo

        Elasticsearch

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        Open Source, Distributed, RESTful Search Engine
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        PROS OF ELASTICSEARCH
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          Powerful api
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          Great search engine
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          Open source
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          Restful
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          Near real-time search
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          Free
        • 85
          Search everything
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          Easy to get started
        • 45
          Analytics
        • 26
          Distributed
        • 6
          Fast search
        • 5
          More than a search engine
        • 4
          Great docs
        • 4
          Awesome, great tool
        • 3
          Highly Available
        • 3
          Easy to scale
        • 2
          Potato
        • 2
          Document Store
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          Great customer support
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          Intuitive API
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          Nosql DB
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          Great piece of software
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          Reliable
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          Fast
        • 2
          Easy setup
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          Open
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          Easy to get hot data
        • 1
          Github
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          Elaticsearch
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          Actively developing
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          Responsive maintainers on GitHub
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          Ecosystem
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          Not stable
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          Scalability
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          Community
        CONS OF ELASTICSEARCH
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          Resource hungry
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          Diffecult to get started
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          Expensive
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          Hard to keep stable at large scale

        related Elasticsearch posts

        Tim Abbott

        We've been using PostgreSQL since the very early days of Zulip, but we actually didn't use it from the beginning. Zulip started out as a MySQL project back in 2012, because we'd heard it was a good choice for a startup with a wide community. However, we found that even though we were using the Django ORM for most of our database access, we spent a lot of time fighting with MySQL. Issues ranged from bad collation defaults, to bad query plans which required a lot of manual query tweaks.

        We ended up getting so frustrated that we tried out PostgresQL, and the results were fantastic. We didn't have to do any real customization (just some tuning settings for how big a server we had), and all of our most important queries were faster out of the box. As a result, we were able to delete a bunch of custom queries escaping the ORM that we'd written to make the MySQL query planner happy (because postgres just did the right thing automatically).

        And then after that, we've just gotten a ton of value out of postgres. We use its excellent built-in full-text search, which has helped us avoid needing to bring in a tool like Elasticsearch, and we've really enjoyed features like its partial indexes, which saved us a lot of work adding unnecessary extra tables to get good performance for things like our "unread messages" and "starred messages" indexes.

        I can't recommend it highly enough.

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        Tymoteusz Paul
        Devops guy at X20X Development LTD · | 23 upvotes · 8.7M 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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        Filebeat logo

        Filebeat

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        PROS OF FILEBEAT
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            Apache Flume logo

            Apache Flume

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            A service for collecting, aggregating, and moving large amounts of log data
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            PROS OF APACHE FLUME
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