Gravwell vs Stroom
Overview
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CLI (Node.js)
Manual
Detailed Comparison
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. | It is a data processing, storage and analysis platform. It is scalable - just add more CPUs / servers for greater throughput. It is suitable for processing high volume data such as system logs, to provide valuable insights into IT performance and usage. |
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;
Analysts and data scientists have access to raw entry records for retroactive analysis and application of machine learning that did not exist at the time of collection;
Capable of data separation and fine-grained access controls for multi-tenancy;
Data collectors or agents are modifiable by the customer to enable processing, filtering, or enrichment before forwarding to the central store;
Massive scalability. Over 100 Terabytes a day is no problem. ;
Unlimited data ingestion;
Unlimited retention;
Live Dashboards;
Secure and Proprietary;
Offline ("Cold") and online ("Hot") replication;
Region-aware redundancy;
Multi-tenancy Permissions & Unlimited user seats;
Binary data support;
Configurable data retention and automatic age-out;
Distributed web frontends;
Unlimited search count | Receive and store large volumes of data such as native format logs. Ingested data is always available in its raw form; Create sequences of XSL and text operations, in order to normalise or export data in any format. It is possible to enrich data using lookups and reference data; Easily add new data formats and debug the transformations if they don't work as expected; Create multiple indexes with different retention periods. These can be sharded across your cluster; Run queries against your indexes or statistics and view the results within custom visualisations; Record counts or values of items over time |
Statistics | |
GitHub Stars - | GitHub Stars 452 |
GitHub Forks - | GitHub Forks 62 |
Stacks 5 | Stacks 1 |
Followers 9 | Followers 3 |
Votes 11 | Votes 0 |
Pros & Cons | |
Pros
Cons
| No community feedback yet |
Integrations | |
| No integrations available | |
What are some alternatives to Gravwell, Stroom?
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Loggly
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Apache Spark
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Logentries
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Logstash
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Presto
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Sematext
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