data-infrastructure
Latest stories covering data-infrastructure, with summaries and links to related coverage.
3 stories · 3 reports · page 1 of 1
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Ontology-Driven Observability: Building an End-to-End Knowledge Graph at Netflix
research
14 min read
Netflix has built an Observability Ontology, a machine-readable knowledge graph intended to connect operational data that is otherwise split across telemetry, alerting, incident, deployment, on-call and discussion systems. The system targets investigations across infrastructure that processes more than 2 billion daily requests and over 38 million real-time logging events per second; Netflix delivered the November 2024 Jake Paul–Mike Tyson fight to roughly 65 million concurrent streams. Netflix says a home-screen rendering regression can take four hours to diagnose and fix, largely because engineers must reconstruct relationships among disconnected systems. The ontology defines typed entities and relationships for applications, incidents, deployments and other operational domains using RDF, OWL, SHACL and SPARQL. Its knowledge graph is stored in Netflix’s QuipuDB, a distributed RDF quad store built on the Rust-based redb key-value database. Applications are validated against a software catalog with roughly 49,000 entries, and the model uses twelve namespaces for operational domains. Dozens of sequential enrichers convert noisy telemetry and incident Slack discussions into constrained triples. A correlation engine evaluates 13 signal types, scores event chains by timing and severity within a 10-minute window, and discards chains below 0.6 confidence. Netflix’s AutoRCA agent uses graph-backed hypotheses and associated evidence to help engineers investigate incidents, while humans retain responsibility for evaluating diagnoses and implementing fixes.
Beating GPT-5.6 Sol on retrieval with 100x cheaper open models
tool
5 min read
Castform utilizes Neon to efficiently transform raw data into usable formats, allowing agents to read, search, and mutate data at scale. A "good agent" requires strong capabilities in context to find relevant data and in model performance to determine search criteria.
Composition Shouldn't be this Hard
opinion
19 min read
Cambra addresses the challenges in data composition and system development, highlighting the gap between the elegance of programming languages and the complexities of real-world applications. It aims to simplify the development and operation of data infrastructure to reduce stress and improve system reliability.