In-Cluster Processing with Edge Delta

In-Cluster Processing is the handling and processing of log data directly within the same computing cluster where the applications and services are running. This approach offers numerous benefits, including cost savings, reduced latency, and quicker access to insights. In-Cluster Processing helps you manage your observability data more effectively while conserving resources and maximizing the value derived from data. By applying this practice, teams can achieve a more agile, secure, and cost-effective approach to log management. The Edge Delta agent is installed in your environment as close as possible to your data sources.

In many cloud environments, data transfer costs (egress fees) are a substantial part of the operating expense. By processing logs in-cluster, the need to send large volumes of log data across network boundaries, which often incurs additional costs, is eliminated or significantly reduced.

Processing log data closer to where it is generated minimizes the distance data has to travel. This proximity between the source of the logs and the processing layer leads to lower latency, as there is less network transit involved. The result is faster log processing and more timely access to processed data. When logs are processed within the cluster, insights such as error identification, performance metrics, or security threats can be gleaned almost instantaneously. Fast data processing feeds into monitoring and alerting systems more rapidly, allowing for quicker operational responses and decision-making.

By maintaining the log processing within the cluster, the data pipeline is simplified. The need for complex data routing to external systems is minimized, making the architecture easier to maintain and understand.

Keeping data processing in-cluster can be advantageous from a data sovereignty and compliance perspective. For regulated industries or applications subject to strict data residency requirements, in-cluster processing ensures that data does not leave the jurisdictional boundaries. In-cluster processing reduces the exposure of data to external networks, shrinking the attack surface and potentially reducing security risks. It allows for tighter control over data access and can improve overall data security posture.

When processing needs grow, scaling up resources within the cluster can be more straightforward and faster than scaling across clusters or to external systems. In-cluster processing aligns well with the elastic scaling properties of cloud-native architectures, enabling dynamic resource allocation based on demand.