Your IT systems are telling you something.
Make sense of it with MELT.
Today’s complex, cloud-native systems create a cacophony of signals that can be hard to translate into actions that support operational and business objectives. Without filtering and synchronizing the noise, insight is lost behind fragments of useful, but often one-dimensional, information.

Within a full stack observability framework, correlating a broad spectrum of data from across environments, applying analytical models, and asking the right questions can unlock system value and contribute to true delivery excellence. MELT is integral to this approach, supporting the next level of system observability.

Learn more about observability
(Metrics, Events, Logs & Traces)

MELT data are the raw materials of this dynamic system management and the beginnings of a deeper understanding of your IT.
of respondents state that data collection and correlation is difficult for their organizations 1 "An Executive Blueprint for an Observability Platform: Driving Operational Excellence and Business Outcomes through Analytics and Automation." IDC.
of technologists say it will continue to be a significant challenge to cut through the noise caused by increasing volumes of data to identify roots causes or performance issues 2 "What is observability?"
Visibility is key
More components. More endpoints. More actors within a more complex system landscape.

The MELT data this instrumentation produces is foundational to revealing the internal state of your systems and yielding insight to support developer decision-making, issue resolution, and continuous improvement to applications.
of organizations said that collecting and correlating data across domains is the #1 most important capability of an observability solution 3 "Innovation Insight for Observability." Gartner.
Breaking down MELT

Each element of MELT contributes information to a holistic system understanding, and each aides application development, trouble-shooting, root-cause analysis, and performance fine-tuning in a specific way:
Numeric measurements grouped or collected at regular intervals or over a given time span.

Use them to:
•   Aggregate values from actions.
•   Monitor KPIs.
•   Alert on anomalous activities.
A discrete action happening at a moment in time. Abstracted yet still highly detailed, these are critical for observability.

Use them to:
•   Identify specific actions.
•   Confirm something happened.
Strings of unstructured text with an associated timestamp describing a system action. The original data type.

Use them to:
•   Debug, troubleshoot.
•   Recreate actions around an event.
•   Answer questions about
access activities.
Chains of events among different components in an application.

Use them to:
•   Understand interdependencies.
•   Follow an app's call chain.
•   Find the root cause of an issue.
Observability's next level:
Traces stitch together component
events to achieve observability

Traces, short for distributed tracing, are navigation markers that help developers and operations teams understand how applications act. They expose the connections between dependent components in a request-response chain. With more microservices, containers, virtual environments, and APIs in the application stack, understanding these links can accelerate development, speed issue resolution, and support system performance refinements, all of which help developers better meet the demands on their time and their applications.
MELT supports full stack observability and informed decision-making
It's no longer necessary to view data in isolation. MELT data can generate the insight necessary for:
  • Shortening time to market by informing developer decisions earlier in the release cycle.

  • Understanding how applications execute user requests across the technology stack.

  • Pinpointing the root cause of issues within a request-response chain.

  • Refining performance to better meet business and customer expectations.

  • Improving the customer experience using real-world data.
By correlating system-wide data, organizations can begin to paint a fuller picture of system functioning across historic domain silos, leading to better communication and greater teamwork among previously disjointed IT functions. This shared, single source of truth is highly valued by organizations and a boon for developer learning and agility.
of IDC survey respondents agreed that a single source of truth must be established for use by all mission-critical IT management tools and teams 4 "An Executive Blueprint for an Observability Platform: Driving Operational Excellence and Business Outcomes through Analytics and Automation." IDC.
of survey respondents said that when IT teams use the same observability tools and share observability data across domains, it fosters teamwork and operational success 5 "An Executive Blueprint for an Observability Platform: Driving Operational Excellence and Business Outcomes through Analytics and Automation." IDC.
As companies move from traditional monitoring solutions to observability solutions that help to answer the 'whats' and 'whys' of system behavior, MELT data from across the technology stack will prove essential to understanding what your system is telling you. Learn how to integrate MELT data into your FSO solution.