Alternatives to Splunk Enterprise logo

Alternatives to Splunk Enterprise

Splunk Cloud, Solarwinds, Power BI, Splunk, and Logstash are the most popular alternatives and competitors to Splunk Enterprise.
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What is Splunk Enterprise and what are its top alternatives?

Splunk Enterprise delivers massive scale and speed to give you the real-time insights needed to boost productivity, security, profitability and competitiveness.
Splunk Enterprise is a tool in the Log Management category of a tech stack.
Splunk Enterprise is an open source tool with GitHub stars and GitHub forks. Here’s a link to Splunk Enterprise's open source repository on GitHub

Top Alternatives to Splunk Enterprise

  • Splunk Cloud

    Splunk Cloud

    If you're looking for all the benefits of Splunk® Enterprise with all the benefits of software-as-a-service, then look no further. Splunk Cloud is backed by a 100% uptime SLA, scales to over 10TB/day, and offers a highly secure environment. ...

  • Solarwinds

    Solarwinds

    Developed by network and systems engineers who know what it takes to manage today's dynamic IT environments, SolarWinds has a deep connection to the IT community. ...

  • Power BI

    Power BI

    It aims to provide interactive visualizations and business intelligence capabilities with an interface simple enough for end users to create their own reports and dashboards. ...

  • Splunk

    Splunk

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

  • 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. ...

  • 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. ...

  • 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. ...

  • SLF4J

    SLF4J

    It is a simple Logging Facade for Java (SLF4J) serves as a simple facade or abstraction for various logging frameworks allowing the end user to plug in the desired logging framework at deployment time. ...

Splunk Enterprise alternatives & related posts

Splunk Cloud logo

Splunk Cloud

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Easy and fast way to analyze valuable machine data with the convenience of software as a service (SaaS)
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PROS OF SPLUNK CLOUD
  • 7
    More powerful & Integrates with on-prem & off-prem
  • 3
    Powerful log analytics
  • 2
    Free
  • 1
    Pci compliance
  • 1
    Production debugger
CONS OF SPLUNK CLOUD
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    related Splunk Cloud posts

    Solarwinds logo

    Solarwinds

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    Unlock powerful workflows, automation, and reporting
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    PROS OF SOLARWINDS
      Be the first to leave a pro
      CONS OF SOLARWINDS
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        Power BI logo

        Power BI

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        Empower team members to discover insights hidden in your data
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        PROS OF POWER BI
        • 6
          Cross-filtering
        CONS OF POWER BI
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          Looking for the best analytics software for a medium-large-sized firm. We currently use a Microsoft SQL Server database that is analyzed in Tableau desktop/published to Tableau online for users to access dashboards. Is it worth the cost savings/time to switch over to using SSRS or Power BI? Does anyone have experience migrating from Tableau to SSRS /or Power BI? Our other option is to consider using Tableau on-premises instead of online. Using custom SQL with over 3 million rows really decreases performances and results in processing times that greatly exceed our typical experience. Thanks.

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

          related Splunk posts

          Shared insights
          on
          Kibana
          Splunk
          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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          PROS OF LOGSTASH
          • 67
            Free
          • 18
            Easy but powerful filtering
          • 12
            Scalable
          • 2
            Kibana provides machine learning based analytics to log
          • 1
            Great to meet GDPR goals
          • 1
            Well Documented
          CONS OF LOGSTASH
          • 3
            Memory-intensive
          • 1
            Documentation difficult to use

          related Logstash posts

          Tymoteusz Paul
          Devops guy at X20X Development LTD · | 23 upvotes · 4.5M 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 · 607.8K 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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          ELK logo

          ELK

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          The acronym for three open source projects: Elasticsearch, Logstash, and Kibana
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          PROS OF ELK
          • 13
            Open source
          • 3
            Good for startups with monetary limitations
          • 2
            Can run locally
          • 1
            Easy to setup
          • 1
            External Network Goes Down You Aren't Without Logging
          • 0
            Json log supprt
          • 0
            Live logging
          CONS OF ELK
          • 4
            Elastic Search is a resource hog
          • 3
            Logstash configuration is a pain
          • 1
            Bad for startups with personal limitations

          related ELK posts

          Wallace Alves
          Cyber Security Analyst · | 1 upvote · 575K views

          Docker Docker Compose Portainer ELK Elasticsearch Kibana Logstash nginx

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          Papertrail logo

          Papertrail

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          Hosted log management for servers, apps, and cloud services
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          PROS OF PAPERTRAIL
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            Log search
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            Easy log aggregation across multiple machines
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            Integrates with Heroku
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            Simple interface
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            Backup to S3
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            Easy setup, independent of existing logging setup
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            Heroku add-on
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            Command line interface
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            Alerting
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            Good for Startups
          CONS OF PAPERTRAIL
          • 2
            Expensive
          • 1
            External Network Goes Down You Wont Be Logging

          related Papertrail posts

          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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          SLF4J logo

          SLF4J

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          Simple logging facade for Java
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          PROS OF SLF4J
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            CONS OF SLF4J
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