What is Edge Delta?

Edge Delta is a new way to do observability. We process your data as it’s created and give you the freedom to route it anywhere. As a result, you can make observability costs predictable, surface the most useful insights, and shape your data however you need.

You can use Edge Delta for monitoring your workloads, querying large-scale log datasets, or creating observability pipelines.

Architecture

Edge Delta operates through an efficient software agent deployed directly within your computing infrastructure, such as Kubernetes clusters. This deployment strategy places it close to the data sources, facilitating immediate analysis and data optimization at the origin. The Edge Delta agent takes on the role of preprocessing data, which includes extracting insights, generating alerts, creating summarized datasets, and performing additional tasks. The processed data is then transmitted to various endpoints in a streamlined manner, including Edge Delta’s analytical backend, external monitoring tools, or data storage solutions.

Key components of Edge Delta’s processing workflow are the Compactor and Rollup services, which are fundamental to enhancing data handling and optimization. The Compactor service, in particular, is designed to compress and encode data such as metrics and logs into efficient formats.

Note: To ensure this component functions effectively, it requires a Persistent Volume Claim (PVC) to be provisioned within the Kubernetes environment. This requirement is critical for the Compactor to properly manage state and prevent data loss, as it prepares the data for transmission to the Edge Delta backend, ensuring judicious bandwidth utilization and improved data processing efficiencies.

Meanwhile, the Rollup service aggregates metric data by optimizing data point frequency and cardinality, which notably reduces storage needs and can accelerate data retrievals.

Features

Using the Edge Delta backend UI unlocks a series of features, including:

• Kubernetes Overview to visualize the health and volume of data being generated by each of your Kubernetes resources.
• Log Search to store and query any volume of data without going over budget.
• Workloads to monitor the “golden signal” metrics and relevant log data across all workloads.
• Patterns to cluster together repetitive loglines. This feature makes it easy to interpret each log message, understand how frequently it’s occurring, and whether it needs to be investigated further.

All of these features are delivered out-of-the-box, so you can hit the ground running and build a launchpad to complete observability.

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