patrikbraborec

with stock as (
    select id, symbol, date, to_json(data) as data_json from input_stage.stock
),

final as (
    select
        id,
        symbol,
        date,
        CAST(json_extract_path_text(data_json, '1. open') as float) as open,

patrikbraborec

SELECT RUNAVG({fact/close}) ROWS BETWEEN 20 PRECEDING AND CURRENT ROW

patrikbraborec

predictions = predict_model(best, data=report_data)
print(predictions.head())

patrikbraborec

 - name: revenue
   label: Revenue
   model: ref('revenue_all')
  
   calculation_method: sum
   expression: price * quantity
 
   timestamp: date
   time_grains: [day, week, month, quarter, year]
 

patrikbraborec

with customer_group as (
   SELECT customer_id, sum(revenue) AS revenue_sum
   FROM {{ metrics.calculate(
       metric('revenue'),
       grain='day',
       dimensions=['customer_id']
   ) }}
   GROUP BY customer_id
),
 

patrikbraborec

SELECT (SUM({fact/price} * {fact/quantity})) WHERE {label/order_status} = "Delivered"

patrikbraborec

SELECT {metric/gd_revenue} WHERE TOP(10%) OF ({metric/gd_revenue})

patrikbraborec

SELECT (
  SELECT {metric/gd_revenue} WHERE (
    SELECT {metric/revenue_top_10} BY {label/customers.customer_id} ALL OTHER
  ) > 0
) / {metric/gd_revenue}

patrikbraborec

name: Run extract and load
on:
 push:
 schedule:
   - cron: "0 2 * * *"
 workflow_dispatch:
jobs:
 run_extract_load:
   runs-on: ubuntu-latest
   steps:

patrikbraborec

with deals as (select id, created_date, to_json("item") as item_json from input_stage.airtable_crm_deals),

final as (
   select
       id,
       CAST(json_extract_path_text(item_json, 'Name') as VARCHAR) as name,
       CAST(json_extract_path_text(item_json, 'Stage') as VARCHAR) as stage,
       CAST(json_extract_path_text(item_json, 'Amount') as NUMERIC) as amount,
       CAST(json_extract_path_text(item_json, 'Details') as VARCHAR) as details,
       CAST(json_extract_path_text(item_json, 'Close Probability') as NUMERIC) as close_probability,