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TELEMETRY Pipelines
With a Mezmo Telemetry Pipeline you can ingest telemetry data from a wide variety of sources, profile it to gain insights into its composition, and then use a variety of processors to transform, reduce, and route it to multiple tools. If your data includes Personally Identifying Information (PII), you can also redact and encrypt it to meet data compliance requirements before sending it to your observability tools or storage.
• Mezmo Telemetry Pipelines Overview
• Getting Started with Mezmo Telemetry Pipelines
• Build and Deploy a Mezmo Telemetry Pipeline
• Set Up Pipeline Sources
• Set Up Pipeline Destinations
• Set Up Pipeline Processors
• Pipeline Examples and Tutorials
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Data
Profiling
With Mezmo’s Data Profiling features, you can understand the composition of your data, identify common message patterns, and optimize the processing of those messages to reduce log volume and extract meaningful information.
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EXAMPLE 1
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Next Gen Log Management
Next Gen Log Management Log management powered by a telemetry pipeline helps you collect all the data you need, separate signals from noise, and search and analyze telemetry data more quickly. Our Log Management Product Guide provides a complete overview of all our features, including the ability to search telemetry data, create views and graphs for data, and set alerts and notifications.
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WHAT IS Mezmo TELEMETRY PIPELINE
Ready to get started with the Mezmo Platform? This guide can help you set up and start using Mezmo in less than 15 mins!
You can also check out our Demo Pipeline that’s included in the Mezmo Web App! This topic in our Mezmo Product Guide will provide a complete overview of the pipeline architecture, how to send demo log data through the pipeline, and observe how it is processed through each step and branch.
Interactive Demos
Want to learn more about how Mezmo’s data processors and features can help you understand, optimize, and respond to your telemetry data?
Check out our interactive demos!
- Filter: Use the Filter Processor to drop events that may not be meaningful or to reduce the total amount of data forwarded to a subsequent processor or destination.
- Reduce: Take multiple log input events and combine them into a single event based on specified criteria. Use Reduce to combine many events into one over a specified window of time.
- Sample: Send only the events required to understand the data.
- Dedupe: Reduce “chatter” in logs. The overlap of data across fields is the key to having the Dedup Processor work effectively. This processor will emit the first matching record of the set of records that are being compared.
- Route: Intelligently route data to any observability, analytics, or visualization platform.
Kubernetes Data Optimization
Our Practitioner’s Guide to Data Optimization includes an example of a telemetry pipeline designed specifically for Kubernetes data optimization. Check it out for an overview of the pipeline architecture, the processor configurations, and instructions for installing an OTel collector so you can try it out with your own data.
- Event to Metric: Create a new metric within the pipeline from existing events and log messages.
- Parse: Various parsing options available to create multiple operations such as convert string to integers or parse timestamps.
- Aggregate metrics: Metric data can have more data points than needed to understand the behavior of a system. Remove excess metrics to reduce storage without sacrificing value.
- Encrypt: Use the Encrypt Processor when sending sensitive log data to storage, for example, when retaining log data containing account names and passwords.
Product Guides
Want more detailed information about all aspects of the Mezmo Platform? Check out our product guides at docs.mezmo.com.
Our guide for Mezmo Telemetry Pipelines also includes a complete list of our supported sources and destinations for telemetry data, along with the processors you can use to transform and optimize your data.
- Use simulation to test your pipelines before you deploy
- Monitor the health of pipelines with OOB dashboards.
- Derive metric data from Logs by parsing the log data to extract specific information.
- Count specific events within the log data and use that count to create a metric.