event-processing

RoadSafety BC DataHub API. A series of Python components for handling and processing digital forms.

Open-the-chests is a training environment for event-patterns recognition.

An application-agnostic, open-source tool designed to build incremental indices from continuously streaming event data.

Offline security replay with traceable detections, reproducible evidence, and indexed/full-scan verification.

Python challenge: merge user events into sessions with deep meta merge, ordered type counts, and Dockerized tests.

An event driven backend that generates and publishes smartwatch health metrics (heart rate, temperature, SpO₂) to AWS IoT Core. Useful for testing IoT pipelines, analytics, and real-time data processing

A Wishbone output module to write data to the Elasticsearch document store

Minimalistic event processing framework on pure Python. Supports async and sync modes.

🚀 Simplify data ingestion with Smart Ingest Kit, a lightweight toolkit that uses intelligent chunking for optimized, layout-aware RAG processes.

Real-time Event-Driven Automation Platform using GitHub Webhooks, n8n, Flask, Docker, and REST APIs for automated repository event processing and workflow orchestration.

Realtime Streaming Analytics - Professional Python project

Arion is a service to transform a set of Esper EPL queries into EQMN models. With a graphical model, even non-computer scientists can comprehend event queries, event types and the cause for an event.

Turn front-door events into decisions a person can act on. Ring webhooks and history in, routines learned, expected visitors matched, then ignore / log / notify / escalate with human-gated actions. Python, SQLite, optional Amazon Bedrock reasoning.

Production-grade streaming analytics platform leveraging Kafka, Spark, PostgreSQL, and Docker for real-time ingestion, processing, monitoring, and risk analysis of global events.

AWS Lambda solution that automatically creates and manages Dead Letter Queues (DLQs) for EventBridge rules. Ensures failed events are captured for analysis with scheduled reconciliation and real-time enforcement.

Operational event processing with PyFlink, Kafka, SLOs, and DLQ

A command-line utility for analyzing timestamped event logs and producing time-based activity summaries. This tool was built as a foundational analytics utility and learning project, focusing on schema-first design, separation of concerns, and incremental feature development.

Python event monitoring demo with synthetic telemetry, configurable alert rules, SQLite, idempotent ingestion, REST API, tests and CI.

Event processing primitives for Python: flexible ingestion, identity resolution, and profile building.