Banking example
The below banking example enriches events using the linked contextual data and metrics
Objective
The below examples aims to showcase how an existing stream pipeline can be enriched with contextual data.
We will apply a query named company_info
to look up data and enrich simultaneously the company quote using the function BidMovingAverage
.
Review the Anatomy of enrichment to understand how an enricher is build.
Initialisation and pre-processing stage
Import company reference data at startup using a parquet file.
Deploy metrics definition to Metrics engine. Company quote metrics are calculated after 1 minute using a three minute tumbling window.
Group processing by company nasdaq
symbol
field.
Stream processing
Save events in to the quotes internal in-memory database every 5 seconds and recreates non-unique index.
Enrichment happens:
Add
company_info
to the event using a query with the event symbol field as the look up value.Add company quote metrics using the
BidMovingAverage
metrics family and the event symbol value.
Publish aggregated view of company quote metrics along with basic company information.
Expected output
The previous example generates the following output, in this case a "|" separatedCSV
.
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