Top Reasons Behind the Rapid Growth in Log Data
As businesses and organizations become more digitized, IT infrastructures are growing increasingly complex, making observability more critical than ever. Logs play a pivotal role in maintaining visibility into these systems, and their volume is growing rapidly—at a compound annual growth rate (CAGR) of 28%.
In this blog, we will explore the main reasons behind this surge in log volume.
Cloud-Native Infrastructure: Ubiquitous across public, private, hybrid environments
Cloud-native technologies, such as containers and Kubernetes, are major contributors to the increase in log volume, primarily due to their transient nature. According to Gartner, the average lifespan of a container is just 10 seconds, which leads to a substantial increase in log generation at the infrastructure level. Each instance, even if short-lived, creates logs that need to be tracked.
Microservices Architecture became a must
The shift to microservices architecture has drastically increased the amount of log data. Every microservice is designed to operate independently and must provide detailed insights into its health, performance, and interactions with other services. While this offers greater flexibility and scalability, it also creates more complexity and a higher volume of logs, metrics, and traces that must be monitored and analyzed. To manage this effectively, observability platforms need to adopt innovative strategies to process and interpret the growing data volume.
Regulatory Compliance is in every vertical, in every industry
Many industries are facing tighter regulations regarding data retention, especially in highly regulated sectors like finance, healthcare, and government. Compliance requirements now demand that organizations retain logs for longer periods, resulting in more data storage and management.
Automation and Orchestration
In DevOps-driven environments, automation and orchestration tools play a significant role in generating additional logs. These systems produce logs to track automated workflows, system states, and deployments. As organizations increase automation, the volume of logs from these tools also rises, requiring more sophisticated log management strategies.
Cloud Adoption and Scalability
With the rise of cloud computing, more organizations are migrating to cloud-based infrastructures, which further drives log generation. Cloud environments are inherently dynamic, with instances being created and terminated based on demand. Each of these instances, whether it’s a virtual machine or container, produces its own set of logs. As organizations scale their environments, the amount of log data multiplies rapidly.
Increased Security Monitoring
As cyber threats become more sophisticated and frequent, organizations are ramping up their security monitoring efforts. This increasedfocus on security generates a vast number of logs, as every system interaction, network request, and potential threat is logged for analysis and incident response.
Changes in Telemetry Landscape
Just a decade ago legacy type logs were the fundamental source of observability and monitoring. With that we refer to Logs elements in the MELT acronym. MELT stands for Metrics, Events, Logs and Traces. Systems don’t generate only logs. They generate other elements of MELT as well and this trend is increasing. The chaining, correlation and parallel processing of different types of MELT elements creates valuable insight and data for observability of the systems.To keep up with the increasing log volume, it’s crucial to implement vendor-independent telemetry pipeline products. These solutions can help manage the growing influx of data, ensuring that your IT team can maintain control and visibility over complex systems.
Ready to learn more about how a telemetry pipeline can transform your log management? Download our free solution brief to see how it can help your IT team stay afloat in the sea of data.
