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We are proud that our open source, appliance-based solution is able to capture data from virtually any production system, knead it into a desired structure, and then feed it to any preferred analytics platform, be it MongoDB or Elasticsearch, through a custom pipeline.


Functioning like a data collector, the solution allows for the unification of the data collection and consumption, and better use and understanding of data. This all-in-one solution running on HP hardware is flexible and modular, allowing it to be used in a large number of varied environments and situations, as allows various options are customisable for end users, scaling up to unlimited amounts of feed and data.​

Collecting and

Aggregating Data

Enterprise Logging

The Henosys enterprise log server appliance has customer-centric logging functions, with features catered to market needs.

Our solution runs typically on HP servers, is powered by Henosys software utilizing opensource solutions, with further enhancement, optimization and configuration to fine- tune in use cases. This enables bidirectional functionality - a destination for devices to send their logs via syslog, as well as to have an ability to actively collect logs on network, devices and services through a network

The disparate sources of information collected can be stored in the same HP server, on a separate on-premise database server, or even buffered to be

written into various cloud database platforms. By default, the collected data will be stored into MongoDB on the same server and a separate data repository to hold the logs is not necessary. Depending on the size of data collected (based on volume and retention policy), the hardware can be a 1U or 2U server with sufficient data storage.

A separate server or group of HP servers can be setup as the data repository for larger volumes. More data can be kept and over a long period of time, and made Highly Available (HA) for disaster recovery. In this configuration, multiple front-end logging appliances can be added to enable distributed logging with much better scaling. Ultimately, for longest term storage or massive volumes of data, the raw logs can be compressed and uploaded into various cloud service providers based on user choice.

Building Your Data Acquisition Layer

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