Log Data Optimization

Most observability vendors use a “centralize-then-analyze” approach, meaning your data must be centralized in their platform before users can search or run analytics on it. This approach worked well before the rise of cloud and microservices. However, now, applications now generate significantly more log data. This creates challenges, such as:

• High Costs: Observability platforms are becoming prohibitively expensive and a simple mistake, like leaving debug logs on, can cause a massive overage.

• Limited Visibility: Teams must sample, filter, or drop loglines to contain costs, inhibiting visibility.

• Poor Performance: As datasets continue to grow, it becomes increasingly difficult to support real-time analytics.

Edge Delta processes your data as it’s created at the source, so you can route optimized datasets instead of trying to compress and ship massive raw log volumes to an observability platform. This approach removes bottlenecks from your architectures to help you improve performance and control costs.

Previously, teams routed raw datasets to their compute resources. Edge Delta flips this model on its head, pushing compute to your data. This allows Edge Delta to derive lightweight analytics from your log data before anything is indexed. As a result, you gain superior visibility and control. More specifically, Edge Delta creates optimized datasets with the following capabilities:

Our customers find data optimization helpful in controlling their data footprint, however there are also times you may need access to full-fidelity log data. Edge Delta fulfills this need by enabling you to:

• Automatically route 100% of your raw logs to low-cost object storage

• Retain your log data for as long as you’d like

• Search on this data at any time from within Edge Delta