You have the data but you don’t know how they are related. Manual computation to identify the relationships among a disparate set of data simply doesn’t scale and meet your needs, and moreover they are error-prone. The challenge is even worse in a highly dynamic and distributed environment. In order to execute the day to day maintenance, planning, troubleshooting activities, a dynamic and multi-dimensional relationship mapping mechanism is required to understand and explore linkage across large amount of operational data that exist across the stacks. Legacy operational tools which are domain specific simply can’t provide you with this view as they lack the broader knowledge of data relationships across the data.
FixStream’s Meridian platform delivers an out-of-the-box data explorer feature to enable users to have a meaningful conversation with the operational data, and to get answers for their day to day operational questions. The solution leverages the huge amount of data collected, correlated and stored in a massively scalable ElasticSearch database designed for quick search across a large number of data elements. This allows the platform to be able to derive many-to-many relationships, hierarchical relationships and composition attributes that exist between the data. Highly contextual, real time and dynamic, this view can enable a large number of data-driven use cases for the enterprise. It relies on different categories of normalized datasets supporting Meridian core functionalities such as inventory, topology and application maps to deliver these features.
You can leverage on-the-spot intelligence to effectively plan for capacity and nightly maintenance. For example, if you want to restart a server, or apply a maintenance patch for a Linux O/S for a particular vendor, you need to quickly know which servers you have to upgrade, what applications they support and what business units may be impacted. When you have 1000s of servers, 100s of applications, 10s of business units, it can be a daunting exercise if done manually, and could take days to complete with accuracy. With Meridian interactive data explorer, you can build these views in seconds with a few clicks.
You can use this view to explore the data discovered by the Meridian platform. You can also ingest data from your CMDB using a simple upload function, and use this feature as your visualization layer for this critical information. The feature is intelligent enough to understand the header and body of the dataset, and to dynamically compute and deliver this view as long as the file is well formatted.
- What are all the entities and configuration items being tracked by Meridian and CMDB
- What are all the various parameters which can be used to categorize and visualize these entities
- Which entities are impacted by changes based on their parameters
- What are all the entities which meet a specific set of categorization parameters