Concept: Washington’s tech startup Edge Delta has developed an edge observability platform for analyzing observability data at its source. The platform leverages distributed stream processing and federated ML for intelligent, dynamic, and automated data processing at the edge.
Nature of Disruption: Edge Delta claims that the new platform automatically analyzes logs, metrics, traces, and event data as it is created at the source. The platform can be deployed as a software agent to process and provide 100% visibility into datasets. This helps teams to route their datasets to appropriate destinations including cloud storage and monitoring platforms. It also helps to reduce observability costs. The observability platform automatically derives insights and identifies anomalies at the source and provides DevOps and SRE (site reliability engineer) with the root cause that enables them to solve issues without running complex queries. It also enables DevOps, SRE, and security teams to identify system behaviors automatically and detect growing hotspots and anomalies, providing more time to resolve problems rather than diagnose them. The startup claims that the new platform enables teams to analyze all of their data rather than just parts of it as they are being analyzed at the edge.
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Outlook: DevOps, SRE, and security teams are required to analyze huge volumes of data, including logs, metrics, events, and traces to identify the vulnerabilities in the system. The architecture of modern databases makes real-time analysis of data difficult with data spread across more distributed and containerized sources including Kubernetes (K8s), Lambda, ECS, and EC2. Edge Delta claims that the new observability platform enables users to analyze data at the source and avoid uploading a large number of datasets on clouds for analysis. In May 2022, Edge Delta raised $63 million in a Series B funding round led by Quiet Capital. The startup aims to use the funding to accelerate corporate growth, including recruitment, marketing, and product innovation.
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