Logentries vs Gravwell: What are the differences?
Developers describe Logentries as "Real-time log management and analytics built for the cloud". Logentries makes machine-generated log data easily accessible to IT operations, development, and business analysis teams of all sizes. With the broadest platform support and an open API, Logentries brings the value of log-level data to any system, to any team member, and to a community of more than 25,000 worldwide users. On the other hand, Gravwell is detailed as "Ingest everything, compromise nothing. Data analytics at scale with predictive pricing". It is the most flexible full-stack analytics platform in the world. We excel at fusing disparate data sources such as firewall logs, end point event logs, network traffic, OT IDS logs, OT process data, threat feed data, etc. to create a central source of knowledge. Created in the IoT age we know modern data insights demand unlimited ingest and analysis capability for cybersecurity, IoT, business analytics, and more. We support a wide range of customers, from energy production, energy delivery, government, finance, and insurance to health and beauty products.
Logentries and Gravwell belong to "Log Management" category of the tech stack.
Some of the features offered by Logentries are:
- Logs as Metrics - Extract field level values, analyze them using powerful search functions, and visualize them with detailed dashboards.
- Dynamic Log Correlation - Dynamically group and correlate your logs in a single dashboard, or aggregate logs from a particular system to give an end-to-end view.
- Live Tail - View your streaming logs in real-time and highlight important events to easily see errors or exceptions in your live data.
On the other hand, Gravwell provides the following key features:
- Ability for deployment in cloud, on-premises, or in an isolated on-premises network lacking outside network connectivity
- Capable of collecting disparate unstructured time-series data sources into a queryable data lake
- Enable data scientists to create custom analysis code/tools to be executed as part of a search pipeline or query system
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