LogPipelines

LogPipelines are software systems that collect, process, and transport log data from diverse sources to a central processing or storage backend. They are typically organized into ingestion, processing, and storage/analysis stages. The ingestion layer gathers logs from servers, applications, containers, and cloud services via agents, forwarders, or API-based collectors. The processing layer parses raw text into structured records, normalizes fields, enriches data with metadata (host, environment, tenant), deduplicates, and may filter or aggregate information. The routing layer directs data to appropriate destinations, applies retention policies, and can partition data by time, source, or tenant. The storage layer provides fast access for search, dashboards, alerting, and downstream analytics, while supporting long-term retention.

Data formats commonly used include plain text, JSON, Syslog, and other structured formats; standardization into a canonical schema facilitates cross-source correlation and querying. Log pipelines can operate in real time, batch mode, or as a hybrid, and may be deployed on premises, in the cloud, or as managed services. They can be centralized for unified visibility or distributed to improve locality and resilience across regions.

Key considerations include scalability and fault tolerance, schema evolution, data quality, privacy and compliance, and cost management. Desired outcomes include reliable delivery, low-latency access, accurate attribution, and robust support for alerting and incident response. Typical use cases cover IT operations monitoring, application observability, security event detection, and compliance auditing. Examples of components or tools in the ecosystem include log shippers, processors, and backends that implement ingestion, parsing, enrichment, routing, and storage.