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Sumo Logic Transaction Analytics
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Sumo Logic Transaction Analytics

Sumo Logic Transaction Analytics : Sumo Logic, announced availability of Transaction Analytics as a component of the Sumo Logic analytics platform to deliver deep visibility into causal relationships across distributed IT systems in order to better inform business decisions. Powered by the Sumo Logic service that scans more than five petabytes of machine data per day, Transaction Analytics takes machine-learning algorithms to the next level by unveiling causal relationships between events as they occur. The Sumo Logic Transaction Analytics capabilities help define and visualize the flows of each transaction, providing real-time and compelling business insights.

Sumo Logic is the next-generation machine data intelligence company that leverages Big Data for real-time IT insights. The company's cloud-based service provides customers like GoGo Inflight, McGraw-Hill, Medallia, Netflix, Orange and SolarCity with real-time interactive analytics at unprecedented petabyte scale. The Sumo Logic service is powered by patent-pending Elastic Log Processing and LogReduce technologies, and transforms machine data into actionable insights for IT operations, application management, and security and compliance teams. Unlike expensive and complex premise-based solutions, the Sumo Logic service has a low TCO, can be deployed instantly, scales elastically and requires zero maintenance.

Transaction Analytics Key Features and Benefits:

• Reduce MTTI (mean time to identification) and expedite root cause analysis by surfacing components of transactions across distributed environments

• Automate processes for collection and analysis of transactional context to decrease time associated with compiling and applying intelligence

• Real-time transaction analysis helps identify and remediate issues before they impact critical systems

• Drive strategic and informed business decisions by identifying core-user behaviors

• Clear visualization to clearly outline complex transactional relationships in real-time

 

 

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