Log data is the most fundamental information unit in our XOps world. It provides a record of every important event. Modern log analysis tools help centralize these logs across all our systems. Log analytics helps engineers understand system behavior, enabling them to search for and pinpoint problems. These tools offer dashboarding capabilities and high-level metrics for system health. Additionally, they can alert us when problems arise. Log analysis tools have evolved over the years, making our lives easier. However, many companies now swim in a sea of log data.

‍

Managing Excessive Log Data

Organizations, for different reasons, can end up in a situation where they’re managing too much log data. For example, a client experienced a sudden quadrupling of log volume, resulting in a surprisingly large bill at the end of the month. The cause was an accidental infrastructure change by an engineer who set the default log level to debug on all services. Such extreme cases are rare but, once discovered, not easy to resolve. Notably, companies have seen an explosion in logs since deploying container technology like Docker and Kubernetes.

Swivel-Chair Ops is Here to Stay (Unfortunately)

To understand the challenges organizations face in managing log data, we conducted a Telemetry Data Strategies survey in collaboration with a popular market research firm. About 100 DevOps executives participated, and the results provided valuable insights.

Our survey revealed that more than 2/3rds of the companies polled use five or more tools to manage incidents, with some navigating incidents using over 20 tools. This abundance of tools creates problems, including expensive infrastructure costs, network slowdowns, and difficulties finding the signal in the noise. Every tool has a reason for being there, and the “single pane of glass concept,” although desirable, is unrealistic.

‍

Tip: You can uncover why the “single pane of glass” is a myth while learning more about how telemetry pipelines can help optimize log data and improve analytics by signing up for our upcoming June 20th webinar, “How to Regain Control of Telemetry Data When There Are Too Many Tools.”

Too Much Data and in the Wrong Format

Respondents identified the top three challenges of managing telemetry data: having too much data, difficulty formatting and normalizing data, and tools that must work together cohesively. These challenges make it harder to derive value from the software and push the limits of existing tools.

How Companies are Responding

Many companies need help addressing these challenges effectively. Initially, organizations invested in building and managing traditional ELK stacks (ElasticSearch, Logstash, and Kibana). However, the increasing costs led to the hiring of dedicated engineers to manage these stacks. To help alleviate the rising numbers, many companies added cloud SaaS solutions like Datadog or Splunk Cloud Platform. While these solutions provided relief on the operations side and improvements in analytics, data volumes continued to rise, resulting in higher bills. The data shows the trend continuing.

Tip: Some of the top challenges respondents of the Telemetry Data Strategies survey shared are an excess of data and tools that don’t interoperate. What are organizations doing to cost-effectively and efficiently use their telemetry data? Download the full report to find out.

‍

Taking Back Control

Despite the difficulties, new technologies and methodologies are emerging to help companies regain control of their log data. Two approaches stand out: standardization through technologies like Open Telemetry (OTEL) and the implementation of telemetry pipelines.

Standardization with Open Telemetry (OTEL)

OTEL involves standard observability protocols for traces, logs, and metrics. While this approach requires changes in code and adoption takes time, it offers long-term benefits, especially in terms of cost relief.

Telemetry Pipelines Offer Immediate Relief

For immediate and impactful results with minimal effort, consider implementing telemetry pipelines. These pipelines sit between your log analysis solution and logging agents, allowing you to filter, transform, and route data to multiple destinations. Think of it as a powerful version of Logstash in the traditional ELK stack. Telemetry pipelines put control of your log data back in your hands.

Conclusion

You're not alone if you struggle with the complexity of telemetry data and observability environments. Consider exploring the trend toward standardization using open telemetry and adopting telemetry pipelines. By embracing these approaches, you can regain control of your log data, tackle the challenges of excessive data, and extract maximum value from your logs. Start your journey toward optimized log data management today!

‍