- Messaging Middleware Platforms
- Log Files
- Data Streams
- Any form of Machine Data
Nastel provides a broad and deep range of methods of ingesting data.
We maintain a GitHub repository of open-sourced, supported connectors that use many methods of connecting to machine data sources. We make use of both agents and agentless methods to fit all needs.
Infrastructure, platforms and applications across datacenters and clouds can be ingested.
Presenting a model of how the data flows through the entire system, how it all connects.
Each form of messaging middleware provides a map of the relationship between the different components of the application stack. By following the message pathway each “transaction” takes through the environment, Nastel DevOps can present the users experience as a topology.
Mapping the data against time to see what happened, when.
Nastel DevOps then presents the performance data that is available through each system that a users request passes through. Showing you the time it took for a request to be serviced.
Identifying signals in the data and understanding what they mean in the context of the business.
Nastel DevOps makes use of Machine Learning Artificial Intelligence (ML AI) to compare the topology of a users experience to the historical record of similar requests, to identify the subtle, early indicators of a performance anomaly.
We make use of the inherent knowledge built into the configuration of messaging middleware systems to deliver DevOps. Instead of trying to recreate complex algorithms to describe the businesses topology, we use the very systems that you have already spent untold blood and gold configuring. By reading the configuration information from across your many messaging middleware environments, and even the contents of the messages themselves, we are able to visualize the entire topology of your enterprise application stack, exactly as your users’ transactions see it. And overlay onto this topology all the data you are already collecting to describe the flow of your business. Then we can compare the historical record of transactions to each new transaction that takes place, allowing any deviance to be recognized before any impact is felt be the user. Using machine learning artificial intelligence (ML AI) we can alert operations staff early enough (predictively or proactively) that remediation can be performed before a event becomes an issue, and using ML AI we create automation to perform these tasks based on alerts or user requests.
Understanding what actions must be taken to fix issues, and how these can be automated.
Using a mix of various machine learning artificial intelligence (ML AL) algorithms, Nastel DevOps can identify the signals associated with complex interactions that indicate a deviance from normal behavior, while also identifying and ignoring false positives. These signals can then be used to alert operators or initiate automated processes to mitigate the potential impact of an event.
Actions taken once to remediate a predicted issue, can then be included in future automations driven by ML AI, as the system learns and improves.
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