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AI and RCA
August 24, 2018 Dan O'Neill, PhD

AI and RCA

It really is getting harder to find the source of a problem in modern applications. Why, modern architectural complexity simply makes it combinatorially difficult to consider possible causes, especially for new (unknown-unknown) problems. Traditional Root Cause Analysis methods really aren’t up to the challenge, are time and resource consuming, and are ultimately intractable for dynamic cloud based applications. What is needed is an intelligent, query free method to find the causal-path from symptoms to cause. This quintessential AI problem is addressed by NMLStream’s specialized Causal Graph technology.

Reading Causal Graphs
August 15, 2018 Peter Pham

Reading Causal Graphs

As the complexity of modern distributed applications increases, problems are becoming increasingly difficult to diagnose. Traditional monitoring and the modern observability trend focus on increasing the quantity of available data. Although this makes it possible to answer most potential queries, it increases the number of possible queries exponentially. Devops still needs a way to see what queries matter for the problem at hand.

How Causal Graphs are different from dashboards
August 6, 2018 Alex Orlova

How Causal Graphs are different from dashboards

DevOps engineers are getting used to wading through a huge amount of metric data and manually manipulating time series. Although this is currently the only way to discover the root cause of problems from applications telemetry, this approach is still highly time and labor intensive. It might take many hours and multiple people to manually check all possible metrics and figure out the root cause.

How to set up alarms and notifications, and how to handle noisy alarms
July 31, 2018 Kristopher Heinrich

How to set up alarms and notifications, and how to handle noisy alarms

The complexity of enterprise applications has been increasing steadily due to the adoption of microservice-oriented architectures and cloud technologies. DevOps teams supporting these infrastructures require a smarter way to manage this growing complexity. NMLStream’s Quadrant uses backward chaining technology to reduce complexity through rapid localization and root cause analysis of problems across all monitored metrics. However, knowing what constitutes a problem remains crucial to understanding the health of a complex distributed system.

Ingesting data into Quadrant
July 26, 2018 Jack Kwan

Ingesting data into Quadrant

Quadrant’s data ingestion mechanism is simple and powerful. As a user, you will be able to gain a new level of understanding of your system through Quadrant’s rich features within minutes. It only takes a few steps to start taking advantage of Quadrant’s capabilities. In general, you will need to provide a mechanism for Quadrant to reach out and make queries to each of your telemetry data source. After that, Quadrant will handle the rest.

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