What is ManageEngine EventLog Analyzer and what are its top alternatives?
ManageEngine EventLog Analyzer is a comprehensive log management solution that helps organizations centrally collect, analyze, and manage event logs from across their network infrastructure. Key features include real-time log collection and correlation, incident management, compliance reports, and log forensic analysis. Limitations include a steep learning curve for beginners, high resource consumption, and limited customization options.
- SolarWinds Log & Event Manager: SolarWinds LEM offers real-time log collection, correlation, and analysis with automated incident response capabilities. Pros include easy-to-use interface and customizable alerting, while cons include high pricing for additional features.
- LogRhythm: LogRhythm provides advanced security analytics and integrated SIEM capabilities. Key features include AI-driven threat detection and response automation. Pros include comprehensive monitoring and threat intelligence feeds, while cons include high initial costs and complex setup.
- Splunk Enterprise: Splunk Enterprise offers a platform for analyzing machine-generated data to gain operational intelligence. Pros include scalability for large environments and extensive integrations, while cons include high licensing costs and resource requirements.
- Graylog: Graylog is an open-source log management solution that offers centralized log collection, processing, and analysis. Pros include a user-friendly interface and community support, while cons include limited enterprise features and maintenance requirements.
- IBM QRadar: IBM QRadar is a SIEM solution that provides threat detection and security incident response capabilities. Key features include advanced analytics and AI-driven insights. Pros include extensive threat intelligence and compliance support, while cons include high costs and complex deployment.
- AlienVault USM: AlienVault USM is a unified security management solution that integrates SIEM, intrusion detection, and threat intelligence. Pros include integrated security tools and threat hunting capabilities, while cons include limited customization options and steep learning curve.
- Netwrix Auditor: Netwrix Auditor offers a platform for data security and compliance analysis with real-time auditing capabilities. Pros include easy deployment and reporting templates, while cons include limited log collection sources and customization options.
- Sumo Logic: Sumo Logic is a cloud-native log management and analytics platform that offers real-time insights and threat detection capabilities. Pros include scalability and dashboard customization, while cons include high costs for large data volumes.
- Loggly: Loggly is a cloud-based log management platform that provides real-time log analysis and monitoring. Key features include log search and analytics tools. Pros include easy setup and scalable pricing, while cons include limited advanced features for enterprise use.
- Rapid7 InsightIDR: Rapid7 InsightIDR is a cloud-based SIEM solution that offers threat detection, incident response, and security analytics. Pros include integrated user behavior analytics and threat intelligence feeds, while cons include limited customization options and high pricing tiers.
Top Alternatives to ManageEngine EventLog Analyzer
- Splunk
It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data. ...
- 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. ...
- 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. ...
- 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. ...
- Logback
It is intended as a successor to the popular log4j project. It is divided into three modules, logback-core, logback-classic and logback-access. The logback-core module lays the groundwork for the other two modules, logback-classic natively implements the SLF4J API so that you can readily switch back and forth between logback and other logging frameworks and logback-access module integrates with Servlet containers, such as Tomcat and Jetty, to provide HTTP-access log functionality. ...
- 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 helps detect, resolve, and avoid infrastructure problems using log messages. Papertrail's practicality comes from our own experience as sysadmins, developers, and entrepreneurs. ...
- 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. ...
ManageEngine EventLog Analyzer alternatives & related posts
- API for searching logs, running reports3
- Alert system based on custom query results3
- Dashboarding on any log contents2
- Custom log parsing as well as automatic parsing2
- Ability to style search results into reports2
- Query engine supports joining, aggregation, stats, etc2
- Splunk language supports string, date manip, math, etc2
- Rich GUI for searching live logs2
- Query any log as key-value pairs1
- Granular scheduling and time window support1
- Splunk query language rich so lots to learn1
related Splunk posts
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.
We are currently exploring Elasticsearch and Splunk for our centralized logging solution. I need some feedback about these two tools. We expect our logs in the range of upwards > of 10TB of logging data.
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- Free69
- Easy but powerful filtering18
- Scalable12
- Kibana provides machine learning based analytics to log2
- Great to meet GDPR goals1
- Well Documented1
- Memory-intensive4
- Documentation difficult to use1
related Logstash posts
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.
Hi everyone. I'm trying to create my personal syslog monitoring.
To get the logs, I have uncertainty to choose the way: 1.1 Use Logstash like a TCP server. 1.2 Implement a Go TCP server.
To store and plot data. 2.1 Use Elasticsearch tools. 2.2 Use InfluxDB and Grafana.
I would like to know... Which is a cheaper and scalable solution?
Or even if there is a better way to do it.
related SLF4J posts
related Logback posts
ELK
- Open source13
- Can run locally3
- Good for startups with monetary limitations3
- External Network Goes Down You Aren't Without Logging1
- Easy to setup1
- Json log supprt0
- Live logging0
- Elastic Search is a resource hog5
- Logstash configuration is a pain3
- Bad for startups with personal limitations1
related ELK posts
Docker Docker Compose Portainer ELK Elasticsearch Kibana Logstash nginx
- Log search85
- Easy log aggregation across multiple machines43
- Integrates with Heroku43
- Simple interface37
- Backup to S326
- Easy setup, independent of existing logging setup19
- Heroku add-on15
- Command line interface3
- Alerting1
- Good for Startups1
- Expensive2
- External Network Goes Down You Wont Be Logging1
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.
- Open-source11
- Great for Kubernetes node container log forwarding9
- Lightweight9
- Easy8