Drasi
Drasi is a data processing platform that simplifies detecting changes in data and taking immediate action. It is a comprehensive solution that provides built-in capabilities to track system logs and change feeds for specific events, evaluate them for relevance, and automatically...
Cost / License
- Free
- Open Source
Platforms
- Self-Hosted
- Kubernetes
- Docker
- PostgreSQL
- Microsoft Azure
Features
- Ad-free
- Database Management Tool
- Continuous Monitoring
Azure integration
- Monitor FileSystem Changes
PostgreSQL support
Azure DevOps integration
Tags
- Microsoft Azure
- change-detection
- system-file-change-detection
- Postgres SQL
- rust-lang
- data-processing
- content-change-detection
Drasi News & Activities
Recent News
- POX published news article about Drasi
Microsoft introduces Drasi, an open source data processing system to detect changesThe Microsoft Azure Incubations team has unveiled Drasi, a new system for data processing that aims...
Recent activities
Drasi information
What is Drasi?
Drasi is a data processing platform that simplifies detecting changes in data and taking immediate action. It is a comprehensive solution that provides built-in capabilities to track system logs and change feeds for specific events, evaluate them for relevance, and automatically initiate appropriate reactions.
Drasi provides real-time actionable insights without the overhead of traditional data processing methods. It tracks system changes and events without the need to copy data to a central data lake or repeatedly query data sources. Drasi uses queries to continuously evaluate incoming data changes. When the changes match the criteria and conditions specified in these queries the result sets of these queries are updated. These updates then trigger context-aware reactions defined tuned to your specific requirements.
Drasi operates through three components:
- Sources connect to data repositories within software systems to monitor logs and feeds to track changing data.
- Continuous Queries interpret monitored changes by applying criteria and conditions to identify significant changes. In Drasi, these Continuous Queries are written using the Cypher Query Language.
- Reactions trigger meaningful responses based on updates to the result sets of the Continuous Queries.
To illustrate how Drasi interprets events and triggers appropriate responses, consider a delivery system for an online ordering service. Orders are processed through an order management system, and delivery drivers need real-time notifications when orders are ready for pickup. Drasi automates this process by:
- Configuring a Source to monitor the order management system for changes in order statuses and a second Source to detect when a driver becomes available for a delivery run.
- Creating a Continuous Query that combines data from both Sources to match orders ready for pickup with available drivers.
- Defining a Reaction to send alerts to drivers, notifying them to proceed to the pickup area. This streamlined setup ensures drivers are promptly informed, optimizing the delivery process through real-time data integration and automated responses.





