Enrichment
Enrich events using connected contextual data, metrics and analytics
Last updated
Enrich events using connected contextual data, metrics and analytics
Last updated
Enrichment is a common practice across industries and Joule makes it simpler.
In advanced streaming scenarios, it's often necessary to add extra contextual data to improve processing accuracy and decision-making.
By using low-latency data stores, streaming events can be quickly enriched with up-to-date information, ensuring fast and responsive data retrieval.
The key concepts and the anatomy of enrichment DSL will deep dive in the concept of enrichment.
Here are some use cases of how it can be applied in stream processing:
Customer data
Adding information such as data bundle plans or credit ratings.
Company metrics
Enriching streams with market ratings or previous day stock sensitivities.
Technical operations
Including data like cell tower signal coverage or machine utilisation limits.
Geofencing
Enriching streams with static geofences for places of interest.
Dynamic triggers
Incorporating real-time business triggers like SLAs or stock price levels.
Domain-specific metrics
Adding general KPIs or metrics for domain-level insights.
Contextual data, usually stored in data marts or warehouses changes slowly. Integrating it into a high-throughput streaming platform can cause processing delays.
These processing delays are often due to the I/O overhead from the network and storage layers. To tackle these challenges, Joule offers a set of optimised solutions. Following enrichers are available.
Metrics
Enrich events with pre-calculated metrics to facilitate further pipeline processing
Dynamic contextual data
Use fast, connected data stores to quickly enrich in-motion events with up-to-date context
Static contextual data
Enrich events with essential slow changing data from Joule’s in-memory database.