ELK Log Analysis vs. XpoLog Log Manager- Analyzer – Monitoring Tool
We performed a comparison between ELK log analysis tool and XpoLog log management tool.
Main points to notice when considering using ELK for your log analysis Vs. XpoLog:
1. XpoLog provides a unique auto mining technology which profiles systems and app log data.
XpoLog tool is able to build automatic IT intelligence which helps to understand the source of the problem and subsequently correlate and compute trends on those problems and then create the search analysis queries for the user. This allows application, systems and security groups to automate their log mining process and time to value on unknown data.
2. Our customers do not need to be superbly technically proficient in the area of searches.
XpoLog brings automation and AI technology to empower the admin – dozens of apps with ready to use reports and dashboards, auto-detection of problems, errors, exceptions, trends and more, predefined filters, monitors, automated log parsing. XpoLog allows simple use of the analytical console to: investigate, discover, and isolate their problems which affect their IT.
3. XpoLog is agentless – for *nix systems we utilize SSH and therefore is able to collect thousands of servers/apps logs without the need for agent installation. This feature is a major advantage for MSPs.
4. The price of our solution is considerably lower than the cost of an Open Source Logs management solution eventually and especially for an enterprise solution. This is a major advantage for customers who are initially put off by the high cost of Splunk or are looking to switch to a more advanced system with a better ROI/TCO.
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|Parameter||ELK Stack (Elastic-search, Logstash, Kibana)||XpoLog|
(Total Cost of Ownership)
- Far from being Free
- Paid support
- Licensing fees for essential components that are usually built-in.
- No security
- Not predictable
- High TCO due to complexity in integration and maintenance
- Enterprise-scale fully featured commercial product
- Offers additional important features such as Analytical Search and Robust security
- Costs much less than ELK
- More sustainable
- More predictable in terms of its future plans
|Hosted solution|| No|| Hybrid|
|On premise|| Yes (but with limited security features)|| Yes|
|Cloud support|| No|| Yes.|
Includes integration with all major cloud providers.
|Data Collection and Parsing|
- An agent-based collection based on Logstash.
- The customer is responsible for sending the data to ELK server
- Requires highly technical expertise.
- Parsing is based on scripts that are developed by the user
- Requires installation on each node and manual configuration.
- Upgrade of agents is very difficult.
- No UI for data collection.
- Even a small-scale deployment may take days.
- Ongoing maintenance and upgrades is extremely difficult.
- Based on SSH.
- Parsing is automatic.
- Includes a comprehensive UI.
|Out of the box applications|| No.|
Relies on a community to build it
Linux, Windows, IIS, Apache, Tomcat, NGINX, Log4net, Log4J, WebSphere, and more
Amazon AWS – S3, CloudFront, CloudTrail, Linux AMI, ELB, RDS,
|Health status|| Requires an additional product license called like Marvel – that is relatively new|| Built-in no additional costs|
|Full Text Search|
- Provided by Elasticsearch and based on Lucene.
- Uses proprietary query language.
- The search is very basic.
- Depending on the manual parsing of the data (see parsing challenges above), any search query may yield a lot of irrelevant results (no sorting by criticality/severity, etc.).
In addition includes Analytical Search, analytical layers on top of the search.
Meaning that the search results are more relevant, include criticality scoring, etc.
|Visualization|| Based on Kibana.|
Visualization is time-based only (no visualization of criticality, etc.).
| Extremely rich visualization including analytical layers that show critical events throughout the timeline.|
Users can easily create dashboards and reports, including charts, geomaps, and more.
|Semantic analysis|| No|| Yes.|
XpoLog uses analytics technologies like machine learning analysis, semantic profiling and anomaly detection on all the gathered logs to establish meaning and importance of the various log event messages.
That knowledge is then used to establish the criticality level of the events.
The critical analysis is used to pro-actively surface important events that require the user’s attention.
|Real-time data|| Depends highly on implementation|| Yes|
|Transaction Analytics|| No|| Yes|
|Extraction of value from the data||Heavily dependent on the user’s experience, knowledge, and acquaintance with a monitored environment.|
In any case, the extraction of value takes time.
|Less dependent on the user’s experience and technical know-how.|
The system proactively surfaces important events that require the user ’s attention.
|Security|| Coming soon|| Yes.|
Enterprise-grade security features.
|Summary|| Considering the cost of the paid components (e.g. Marvel and Shield) and the complexity in deployment and integration, the “open source” solution is definitely not free.|
By adding a little bit to the licensing cost, clients can get XpoLog, which is much more robust and includes important features that enable users to extract more value from the data quickly without relying on their expertise.