How 10 Mezmo Customers Used Telemetry Pipelines to Streamline Data and Cut Noise
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How 10 Mezmo Customers Used Telemetry Pipelines to Streamline Data and Cut Noise
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The Pipeline: From Observability Chaos To Control
The volume of observability data continues to grow as application stacks become more complex, with hybrid cloud environments, microservices, and open-source libraries all contributing to the increase. Much of this data is generated automatically, outside a developer’s control. Because its value is often temporal, you don’t know you need it until you do—and sometimes, you don’t need it at all.
The current paradigm for managing observability data is to collect and store everything in an observability tool, then figure out what’s important later. However, this approach is becoming increasingly unsustainable. Storage and ingestion costs continue to rise rapidly, while the value derived from the data remains largely flat. In fact, many organizations store up to 80% of their log data without ever using it, simply because they worry they might need it someday.
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The Pipeline: From Observability Chaos To Control
The volume of observability data continues to grow as application stacks become more complex, with hybrid cloud environments, microservices, and open-source libraries all contributing to the increase. Much of this data is generated automatically, outside a developer’s control. Because its value is often temporal, you don’t know you need it until you do—and sometimes, you don’t need it at all.
The current paradigm for managing observability data is to collect and store everything in an observability tool, then figure out what’s important later. However, this approach is becoming increasingly unsustainable. Storage and ingestion costs continue to rise rapidly, while the value derived from the data remains largely flat. In fact, many organizations store up to 80% of their log data without ever using it, simply because they worry they might need it someday.
The Pipeline: From Observability Chaos To Control
The volume of observability data continues to grow as application stacks become more complex, with hybrid cloud environments, microservices, and open-source libraries all contributing to the increase. Much of this data is generated automatically, outside a developer’s control. Because its value is often temporal, you don’t know you need it until you do—and sometimes, you don’t need it at all.
The current paradigm for managing observability data is to collect and store everything in an observability tool, then figure out what’s important later. However, this approach is becoming increasingly unsustainable. Storage and ingestion costs continue to rise rapidly, while the value derived from the data remains largely flat. In fact, many organizations store up to 80% of their log data without ever using it, simply because they worry they might need it someday.
The Pipeline: From Observability Chaos To Control
The volume of observability data continues to grow as application stacks become more complex, with hybrid cloud environments, microservices, and open-source libraries all contributing to the increase. Much of this data is generated automatically, outside a developer’s control. Because its value is often temporal, you don’t know you need it until you do—and sometimes, you don’t need it at all.
The current paradigm for managing observability data is to collect and store everything in an observability tool, then figure out what’s important later. However, this approach is becoming increasingly unsustainable. Storage and ingestion costs continue to rise rapidly, while the value derived from the data remains largely flat. In fact, many organizations store up to 80% of their log data without ever using it, simply because they worry they might need it someday.
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