ReplyMap
Eventum.run
Faker
Mockaroo
Mimesis
ShadowTraffic
Tonic AI
Describe events, schedule them, and stream to ClickHouse, OpenSearch, Kafka, files or any HTTP endpoint. Eventum is used for testing pipelines and detection rules, live demos, seeding databases and load testing.
Highlights: - Pipeline of three swappable stages: when events happen, what they contain, where they go - Scheduling from cron and fixed intervals to statistical time patterns - Jinja templates with an extended API (Faker and Mimesis data generators, weighted random helpers, CSV/JSON samples, and more), or Python scripts when templates aren't enough - Stateful generation: three scopes of state plus a finite state machine mode for multi-step scenarios - Parallel fan-out: stdout, files, ClickHouse, OpenSearch, Kafka, any HTTP endpoint - Live mode (events fire at their timestamps) or sample mode (everything at once) - Eventum Studio web UI, REST API, and an MCP server for AI agents
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Eventum.run's answer:
Python (FastAPI, Pydantic, Jinja2) for the engine, CLI and REST API; React + TypeScript for the Eventum Studio web UI. Ships as a pip package and Docker image.
Eventum.run's answer:
Libraries like Faker give you fake values - Eventum gives you the whole pipeline: scheduling, templating, state, and parallel delivery to ClickHouse, OpenSearch, Kafka, files or any HTTP endpoint. And it ships with Eventum Studio, a web UI where you preview and debug events before anything goes live.
Eventum.run's answer:
Most data generators produce random values at a flat rate. Eventum also models behavior: traffic follows cron schedules, intervals or statistical time patterns with peaks, bursts and quiet periods, and templates persist state between events - three scopes of state plus a finite state machine mode for multi-step scenarios like user sessions.
Eventum.run's answer:
Data engineers, SIEM and detection engineers, and developers who need realistic data for testing pipelines, live demos, seeding databases or load testing - teams that would otherwise write throwaway generator scripts.
Eventum.run's answer:
The author works on a data analytics platform similar to Splunk, where every customer demo needs a believable case running on data that looks alive. The team generated demo data with Splunk Eventgen, but the workflow never felt convenient, so around 2023 he started building his own generator. It grew into Eventum, now used by his SIEM team and data engineers daily.
Eventum.run's answer:
Internal SIEM and data engineering teams at the author's Cyber Security company