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May 27, 2016
May 27, 2016
Machine data, in the form of logs and time-series metrics, contains the raw information which can be collected, analyzed and visualized to deliver continuous intelligence. By unifying logs and metrics in a single analytics platform, Sumo Logic provides real-time visibility into the health and viability of your modern applications.
Multiple Tool Approach
Until now, machine data analytics tools were designed either for logs or siloed, time-series metrics. Businesses typically rolled out multiple tools and switched between multiple screens in an attempt to correlate data for monitoring, troubleshooting and insights into usage patterns.
Re-architect or Sacrifice Speed?
In the interests of combining metrics data with log files, many tool providers often take the route of extending existing capabilities to address new data types instead of re-architecting their tool to handle different data types natively. The former approach sacrifices speed and sub-optimizes compute cycles. For example, queries, burdened with architectural limitations, slow down dramatically. And more hardware and compute cycles are required to support the analytics environment, negatively impacting the cost/value equation. In the interests of containing costs, IT organizations may restrict the number of metrics only to find themselves at a loss when problems occur and the needed metrics for troubleshooting are not at their fingertips.
A New Architectural Platform
In order to provide real-time search and analytics, Sumo Logic has invested in a new architectural platform, one that provides the same level of capabilities to structured time-series metrics as semistructured and unstructured logs. This new architecture delivers realtime, side-by-side and contextual insight into machine data for:
• Top-to-bottom visibility through real-time dashboards that integrate custom metrics, AWS CloudWatch and infrastructure metrics and display them in histograms, pie charts and real-time dashboards
• Team collaboration by providing richer and real-time insights based on a shared version of the truth to support DevOps and continuous delivery
• Accelerating and simplifying troubleshooting by applying advanced analytics and machine-learning algorithms to your logs and time-series metrics
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