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A scalable pipeline that ingests application and server logs, processes them in near real time, detects anomalies, and presents operational dashboards.
Problem Statement
Modern applications generate millions of log lines per day. Without a pipeline, teams cannot find errors, slow endpoints, or security incidents quickly.
Objectives
Ingest logs from multiple services through a queue
Parse and aggregate logs in streaming fashion
Detect anomalies in error rates and latency
Visualise metrics on a live dashboard
Modules
Ingestion Layer
Log shippers and a message queue for buffering.
Stream Processor
Parsing, enrichment, and windowed aggregation jobs.
Anomaly Detection
Statistical and ML based alerts on unusual patterns.
Dashboard
Searchable logs, charts, and alert history.
Expected Output
A containerised pipeline with a dashboard showing live request volume, error rates, slow endpoints, and triggered alerts.
Future Scope
Trace correlation, cost-aware storage tiers, and root cause suggestions using language models.