By Kevin Woods

When it comes to Kubernetes, operational telemetry data is critical for SREs to maintain SLOs. The applications emit log data, APM metrics, and trace data in addition to Kubernetes' insights into cluster health and service performance.

Kubernetes and the applications it orchestrates produce extensive telemetry data. This data can be voluminous and not well-understood. It is possible to get some basic metrics and health sentiments using telemetry pipelines. This can be done quickly and easily with a welcome pipeline. However, many see the need to go deeper in understanding their data.

Create a Data Profile

A data profile is a structured overview of telemetry data, revealing patterns, anomalies, and trends. Think of it like a health check or a report card, offering a snapshot of the current state of affairs while showing trends that point to future conditions. The data profile can be a roadmap, guiding the SRE to choose data optimizations and actionable insights derived from vast telemetry data streams.

Kubernetes Data Profiles

Within Kubernetes, specific data sets play a role in assessing performance, health, and security. We can segment this telemetry data into three primary profiles:

• Cluster Performance Profile: A pulse check on metrics like node availability, resource allocation, and pod distribution. This profile helps determine if you're optimizing or overstretching your resources, influencing cost considerations.
• Service Health Profile: This profile monitors service latency, error rates, and request volume. These metrics have tangible impacts on customer experience and revenue streams. Persistent latency issues may signal that you need to reallocate resources to prevent potential customer dissatisfaction.
• Security and Compliance Profile: This profile aggregates events related to security policies. Kubernetes telemetry data can also identify possible risks to private data and suggest transformations that will reduce this specific security risk.
  

Kubernetes Telemetry Data Profiling over Time

One tends to think of Kubernetes or any telemetry data as representing a steady state or repeated patterns at the macro level. Once you have your data profile and pipelines set up, little change is needed, so you would think...

However, the view changes once we detect something that needs attention, such as misallocated resources, unforeseen failures, or specific security events - each can have significant business repercussions.

Addressing Dynamic Business Needs

As the environment is dynamic and changeable, so should the pipeline. The need is often to respond quickly to an anomaly or failure that was not predictable. Telemetry pipelines, such as Mezmo's, that continuously gather and analyze data, are instrumental in this adaptation. Maintaining an updated data profile via these pipelines ensures business alignment between operational demands and Kubernetes resources.

Additionally, with advanced telemetry tools integrated into these pipelines, there's potential for predictive insights-forecasting demand surges or detecting anomalies and, as a result, facilitating proactive strategic shifts.

Telemetry-Driven Cost Management

In a Kubernetes environment, efficiency translates directly to dollars. By understanding and optimizing telemetry data, businesses can save significant costs through transformations like:

1. Filtering out duplicate and extraneous events that don't contribute value to your observability results.
2. Routing a full-fidelity copy of the remaining telemetry data to a long-term retention solution for future auditing or investigation instead of your observability tools.
3. Trimming and transforming events by removing empty values, dropping unnecessary labels, and transforming inefficient data formats into a format specific to your observability destinations.
4. Merging events by grouping messages and combining their fields to retain unique data while removing repetitive data.
5. Condensing events into metrics to reduce the number of hours and resources dedicated to supporting backend tools and convert unstructured data to structured data before indexing to make searches more manageable, faster, and efficient.

Establish a Telemetry Framework

Enterprises need to address their telemetry systems and strategy specifically. To this end, certain foundational elements define a robust telemetry framework. Consider the following pillars as vital components for tomorrow's success:

• Understand your Data Profiles: How is your log data generated and structured? What is its form, sources, and relative value? How will this data change in the case of an incident?
• Implement Data Collection: This involves instrumenting your infrastructure, applications, and devices to collect and send data to your telemetry system. Collecting and processing that data at your edge can also help with data security and privacy concerns.
• Use a Telemetry Pipeline to Make Needed Transformations: A telemetry pipeline can drastically reduce the data quantity flowing to your expensive observability platforms without losing information.
• Ensure Data Integrity and Privacy: If you handle user data, you must respect privacy regulations (like GDPR, CCPA). This might involve removing or protecting user data that inadvertently appears in your telemetry.
• Implement Storage Solutions: Depending on the retention policies and the volume of data, you might need scalable storage solutions. Time-Series Databases (TSDB) like InfluxDB or cloud solutions like AWS S3 might be suitable.
• Be Responsive: Telemetry is not a set-it-and-forget-it solution. As your system grows and you gather more insights, you'll likely want to refine what data you collect, how you analyze it, and how you react to it in real time.
  

Final Thoughts

In the Kubernetes ecosystem, telemetry data is pivotal. With effective management and profiling, businesses unlock granular insights into their current operational landscape and lay the foundation for informed strategic decisions. This approach enables organizations to optimize resources efficiently, anticipate and mitigate potential challenges, and position themselves at the vanguard of their industry.

Embracing telemetry is not just a technical decision; it's a decisive step towards sustainable growth and gaining a competitive edge.

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Join us at KubeCon + CloudNativeCon North America this November 6 - 9 in Chicago for more on Kubernetes and the cloud native ecosystem. 

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ABOUT THE AUTHOR

Kevin Woods, Director of Product Marketing, Mezmo

Kevin Woods is the Director of Product Marketing for Mezmo. Kevin started his career in engineering but moved to product management and marketing because of his curiosity about how users make technology choices and the drivers for their decision-making. Today, Kevin feeds that fascination by helping Mezmo with go-to-market planning, value-proposition development, and content for communications.