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

1. Import company reference data at startup using a parquet file.

2. Deploy metrics definition to Metrics engine. Company quote metrics are calculated after 1 minute using a three minute tumbling window.

3. Group processing by company nasdaq symbol field.

Stream processing

1. Save events in to the quotes internal in-memory database every 5 seconds and recreates non-unique index.

2. Enrichment happens:

   1. Add company_info to the event using a query with the event symbol field as the look up value.

   2. Add company quote metrics using the BidMovingAverage metrics family and the event symbol value.

3. Publish aggregated view of company quote metrics along with basic company information.

stream:
  name: quoteAnalyticsStream
  eventTimeType: EVENT_TIME

  initialisation:
    sql import:
      schema: contextual_data
      parquet:
        - table: nasdaq_companies
          asView: false
          files: [ 'data/parquet/nasdaq.parquet' ]
          index:
            fields: [ 'symbol' ]
            unique: true

  processing unit:
    metrics engine:
      runtime policy:
        frequency: 1
        startup delay: 1
        time unit: MINUTES

      foreach metric compute:
        metrics:
          - name: BidMovingAverage
            metric key: symbol
            table definition: bid_moving_averages (symbol VARCHAR, avg_bid_min FLOAT, avg_bid_avg FLOAT,avg_bid_max FLOAT,createdTimestamp TIMESTAMP)
            query:
              SELECT symbol,
              MIN(bid) AS 'avg_bid_min',
              AVG(bid) AS 'avg_bid_avg',
              MAX(bid) AS 'avg_bid_max'
              FROM quotes.nasdaq
              WHERE
              ingestTime >= epoch_ms(date_trunc('minutes',now() - INTERVAL 3 MINUTES)) AND ingestTime <= epoch_ms(now())
              GROUP BY symbol
              ORDER BY 1;
            truncate on start: true
            compaction policy:
              frequency: 8
              time unit: HOURS

    pipeline:
      - tap:
          target schema: quotes
          flush frequency: 5
          index:
            unique: false
            fields:
              - symbol
      - enricher:
          fields:
            company_info:
              by query: "select * from contextual_data.nasdaq_companies where Symbol = ?"
              query fields: [ symbol ]
              with values: [ Name,Country ]
              using: JouleDB
              
            quote_metrics:
              by metric family: BidMovingAverage
              by key: symbol
              with values: [avg_bid_min, avg_bid_avg, avg_bid_max]
              using: MetricsDB
              
  emit:
    select: "symbol, Name, Country, avg_bid_min, avg_bid_avg, avg_bid_max"

  group by:
    - symbol

Expected output

The previous example generates the following output, in this case a "|" separatedCSV.

event_type|sub_type|event_time|ingest_time|symbol|avg_bid_min|avg_bid_avg|avg_bid_max|company_name|industry
nasdaq_View|null|1709894745240|1709894745240|MTZ|113.97318|103.25833|110.11051|MasTec Inc. Common Stock|Basic Industries
nasdaq_View|null|1709894745244|1709894745244|SBRA|18.71446|13.90475|16.472204|Sabra Health Care REIT Inc. Common Stock|Consumer Services
nasdaq_View|null|1709894745240|1709894745240|MUA|17.244429|14.651974|15.772599|Blackrock MuniAssets Fund Inc Common Stock|Finance