Merge branch 'CEC' into 'master'
Update file Commodity Exchange Codes.xls See merge request deemanone/materia!6
This commit is contained in:
@@ -24,7 +24,7 @@
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"\n",
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"data = \"../data/\"\n",
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"df = pd.read_csv(\"../data/psd_alldata.csv\", encoding=\"latin1\")\n",
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"\"\"\"\n",
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"\n",
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"df.rename(columns={\n",
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" 'Commodity_Description': 'commodity',\n",
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" 'Country_Name': 'country',\n",
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@@ -115,8 +115,7 @@
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" 'Total Distribution', 'Ending Stocks', 'Net Supply',\n",
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" 'Supply-Demand Balance', 'Stock-to-Use Ratio (%)']]\n",
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"combined_global.to_csv(\"global_summary_all.csv\", index=False)\n",
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"print(\"🌐 Combined global summary saved as 'global_summary_all.csv'\")\n",
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"\"\"\""
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"print(\"🌐 Combined global summary saved as 'global_summary_all.csv'\")\n"
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]
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},
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{
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@@ -0,0 +1,64 @@
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/*
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* Silver layer: Pivots the raw PSD data into a wide format,
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* with key attributes ('Production', 'Imports', etc.) as columns.
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* This is equivalent to step 2 of the Python script 03_Extraction.
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*/
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MODEL (
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name transform.sqlmesh_materia.models.staging.stg_psd_alldata_1_filter_silver_layer,
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kind INCREMENTAL_BY_TIME_RANGE (
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time_column ingest_date
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),
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start '2006-08-01',
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cron '@daily'
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);
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SELECT
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commodity_code,
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commodity_name,
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country_code,
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country_name,
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ingest_date,
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-- Replicate the Python script's pivot by using conditional aggregation
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-- This creates a single row for each commodity-country-date combination
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COALESCE(SUM(CASE WHEN attribute_name = 'Production' THEN value END), 0) AS Production,
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COALESCE(SUM(CASE WHEN attribute_name = 'Imports' THEN value END), 0) AS Imports,
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COALESCE(SUM(CASE WHEN attribute_name = 'Exports' THEN value END), 0) AS Exports,
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COALESCE(SUM(CASE WHEN attribute_name = 'Total Distribution' THEN value END), 0) AS Total_Distribution,
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COALESCE(SUM(CASE WHEN attribute_name = 'Ending Stocks' THEN value END), 0) AS Ending_Stocks,
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COALESCE(SUM(CASE WHEN attribute_name = 'Beginning Stocks' THEN value END), 0) AS Beginning_Stocks,
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COALESCE(SUM(CASE WHEN attribute_name = 'Total Supply' THEN value END), 0) AS Total_Supply,
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COALESCE(SUM(CASE WHEN attribute_name = 'Domestic Consumption' THEN value END), 0) AS Domestic_Consumption,
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COALESCE(SUM(CASE WHEN attribute_name = 'Domestic Demand' THEN value END), 0) AS Domestic_Demand,
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COALESCE(SUM(CASE WHEN attribute_name = 'Food Use' THEN value END), 0) AS Food_Use,
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COALESCE(SUM(CASE WHEN attribute_name = 'Industrial Use' THEN value END), 0) AS Industrial_Use,
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COALESCE(SUM(CASE WHEN attribute_name = 'Seed Use' THEN value END), 0) AS Seed_Use,
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COALESCE(SUM(CASE WHEN attribute_name = 'Waste' THEN value END), 0) AS Waste,
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COALESCE(SUM(CASE WHEN attribute_name = 'Feed Use' THEN value END), 0) AS Feed_Use
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FROM transform.sqlmesh_materia.models.staging.stg_psd_alldata_0
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-- Filter for the specific attributes used in the pivot table for efficiency
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WHERE attribute_name IN (
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'Production',
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'Imports',
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'Exports',
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'Total Distribution',
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'Ending Stocks',
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'Beginning Stocks',
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'Total Supply',
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'Domestic Consumption',
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'Domestic Demand',
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'Food Use',
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'Industrial Use',
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'Seed Use',
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'Waste',
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'Feed Use'
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)
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GROUP BY
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commodity_code,
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commodity_name,
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country_code,
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country_name,
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ingest_date
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ORDER BY
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commodity_name,
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country_name,
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ingest_date;
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@@ -0,0 +1,110 @@
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/*
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* Gold layer: Calculates derived metrics like Net Supply, Trade Balance,
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* and Stock-to-Use Ratio based on the pivoted silver layer data.
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* This also includes the global aggregates, mimicking steps 3 and 4
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* of the Python script 03_Extraction.
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*/
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MODEL (
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name transform.sqlmesh_materia.models.staging.stg_psd_alldata_2_filter_gold_layer,
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kind INCREMENTAL_BY_TIME_RANGE (
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time_column ingest_date
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),
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start '2006-08-01',
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cron '@daily'
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);
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-- CTE to calculate country-level derived metrics
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WITH country_metrics AS (
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SELECT
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commodity_code,
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commodity_name,
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country_code,
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country_name,
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ingest_date,
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Production,
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Imports,
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Exports,
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Total_Distribution,
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Ending_Stocks,
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-- Derived metrics per country, mirroring Python script
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(Production + Imports - Exports) AS Net_Supply,
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(Exports - Imports) AS Trade_Balance,
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(Production + Imports - Exports) - Total_Distribution AS Supply_Demand_Balance,
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-- Handle division by zero for Stock-to-Use Ratio
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(Ending_Stocks / NULLIF(Total_Distribution, 0)) * 100 AS Stock_to_Use_Ratio_pct,
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-- Calculate Production YoY percentage change using a window function
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(Production - LAG(Production, 1, 0) OVER (PARTITION BY commodity_code, country_code ORDER BY ingest_date)) / NULLIF(LAG(Production, 1, 0) OVER (PARTITION BY commodity_code, country_code ORDER BY ingest_date), 0) * 100 AS Production_YoY_pct
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FROM transform.sqlmesh_materia.models.staging.stg_psd_alldata_1_filter_silver_layer
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),
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-- CTE to calculate global aggregates by summing up country-level data
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global_aggregates AS (
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SELECT
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commodity_code,
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commodity_name,
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NULL::TEXT AS country_code, -- Use NULL for global aggregates
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'Global' AS country_name,
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ingest_date,
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SUM(Production) AS Production,
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SUM(Imports) AS Imports,
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SUM(Exports) AS Exports,
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SUM(Total_Distribution) AS Total_Distribution,
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SUM(Ending_Stocks) AS Ending_Stocks
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FROM transform.sqlmesh_materia.models.staging.stg_psd_alldata_1_filter_silver_layer
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GROUP BY
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commodity_code,
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commodity_name,
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ingest_date
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),
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-- CTE to calculate derived metrics for global aggregates
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global_metrics AS (
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SELECT
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commodity_code,
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commodity_name,
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country_code,
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country_name,
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ingest_date,
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Production,
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Imports,
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Exports,
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Total_Distribution,
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Ending_Stocks,
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(Production + Imports - Exports) AS Net_Supply,
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(Exports - Imports) AS Trade_Balance,
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(Production + Imports - Exports) - Total_Distribution AS Supply_Demand_Balance,
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(Ending_Stocks / NULLIF(Total_Distribution, 0)) * 100 AS Stock_to_Use_Ratio_pct,
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(Production - LAG(Production, 1, 0) OVER (PARTITION BY commodity_code ORDER BY ingest_date)) / NULLIF(LAG(Production, 1, 0) OVER (PARTITION BY commodity_code ORDER BY ingest_date), 0) * 100 AS Production_YoY_pct
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FROM global_aggregates
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)
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-- Combine country-level and global-level data into a single output
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SELECT
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hkey,
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commodity_code,
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commodity_name,
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country_code,
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country_name,
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ingest_date,
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Production,
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Imports,
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Exports,
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Total_Distribution,
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Ending_Stocks,
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Net_Supply,
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Trade_Balance,
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Supply_Demand_Balance,
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Stock_to_Use_Ratio_pct,
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Production_YoY_pct
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FROM (
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SELECT
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@GENERATE_SURROGATE_KEY(commodity_code, country_code, ingest_date) AS hkey,
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*
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FROM country_metrics
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UNION ALL
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SELECT
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@GENERATE_SURROGATE_KEY(commodity_code, country_name, ingest_date) AS hkey,
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*
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FROM global_metrics
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) AS combined_data
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ORDER BY
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commodity_name,
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country_name,
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ingest_date;
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57
transform/sqlmesh_materia/seeds/commodity_exchange_codes.csv
Normal file
57
transform/sqlmesh_materia/seeds/commodity_exchange_codes.csv
Normal file
@@ -0,0 +1,57 @@
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commodity_name,exchange_code,exchange
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Crude Oil WTI,CL,CME
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Crude Oil Brent,BZ,ICE
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Gasoline RBOB,RB,CME
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Heating Oil,HO,CME
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Natural Gas,NG,CME
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Ethanol,CU,CME
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Cocoa,CC,ICE
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Cotton,CT,ICE
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Orange Juice,FCOJ-A,ICE
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Coffee,KC,ICE
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Lumber,LBR,ICE
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Sugar,SB,ICE
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European Gas TTF,TTF,ICE
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European Union Emissions Allowance,ECF,ICE
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Gold,GC,CME
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Silver,SI,CME
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Platinum,PL,CME
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Copper,HG,CME
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Palladium,PA,CME
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Live Cattle,LE,CME
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Feeder Cattle,GF,CME
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Lean Hogs,HE,CME
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Corn,ZC,CME
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Soybean Oil,ZL,CME
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Soybean meal,ZM,CME
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Oats,ZO,CME
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Rough Rice,ZR,CME
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Soybeans,ZS,CME
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Wheat,ZW,CME
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Canola,RS,ICE
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Rebar,RB,SHFE
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Hot-Rolled Coil,HC,SHFE
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Nickel,NI,SHFE
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Tin,SN,SHFE
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Aluminum,AL,SHFE
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Zinc,ZN,SHFE
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Natural Rubber,RU,SHFE
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Bitumen,BU,SHFE
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Iron Ore,I,DCE
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Palm Oil,P,DCE
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Eggs,JD,DCE
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Coking Coal,JM,DCE
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Polyvinyl Chloride (PVC),V,DCE
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White Sugar,SR,ZCE
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Cotton,CF,ZCE
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Apple,AP,ZCE
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PTA,TA,ZCE
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Methanol,MA,ZCE
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LME Aluminum,AH,LME
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LME Copper,CA,LME
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LME Lead,PB,LME
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LME Nickel,NI,LME
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LME Tin,SN,LME
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LME Zinc,ZS,LME
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Iron Ore,TIO,SGX
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Rubber,TSR,SGX
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@@ -0,0 +1,57 @@
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commodity_name,exchange_code,exchange,commodity_code
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Crude Oil WTI,CL,CME,NA
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Crude Oil Brent,BZ,ICE,NA
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Gasoline RBOB,RB,CME,NA
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Heating Oil,HO,CME,NA
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Natural Gas,NG,CME,NA
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Ethanol,CU,CME,NA
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Cocoa,CC,ICE,NA
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Cotton,CT,ICE,2631000
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Orange Juice,FCOJ-A,ICE,0585100
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Coffee,KC,ICE,0711100
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Lumber,LBR,ICE,NA
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Sugar,SB,ICE,0612000
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European Gas TTF,TTF,ICE,NA
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European Union Emissions Allowance,ECF,ICE,NA
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Gold,GC,CME,NA
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Silver,SI,CME,NA
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Platinum,PL,CME,NA
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Copper,HG,CME,NA
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Palladium,PA,CME,NA
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Live Cattle,LE,CME,0011000
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Feeder Cattle,GF,CME,0011000
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Lean Hogs,HE,CME,NA
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Corn,ZC,CME,0440000
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Soybean Oil,ZL,CME,4232000
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Soybean meal,ZM,CME,0813100
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Oats,ZO,CME,0452000
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Rough Rice,ZR,CME,0422110
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Soybeans,ZS,CME,NA
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Wheat,ZW,CME,0410000
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Canola,RS,ICE,2226000
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Rebar,RB,SHFE,NA
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Hot-Rolled Coil,HC,SHFE,NA
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Nickel,NI,SHFE,NA
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Tin,SN,SHFE,NA
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Aluminum,AL,SHFE,NA
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Zinc,ZN,SHFE,NA
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Natural Rubber,RU,SHFE,NA
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Bitumen,BU,SHFE,NA
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Iron Ore,I,DCE,NA
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Palm Oil,P,DCE,4243000
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Eggs,JD,DCE,NA
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Coking Coal,JM,DCE,NA
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Polyvinyl Chloride (PVC),V,DCE,NA
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White Sugar,SR,ZCE,0612000
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Cotton,CF,ZCE,2631000
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Apple,AP,ZCE,0574000
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PTA,TA,ZCE,NA
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Methanol,MA,ZCE,NA
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LME Aluminum,AH,LME,NA
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LME Copper,CA,LME,NA
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LME Lead,PB,LME,NA
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LME Nickel,NI,LME,NA
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LME Tin,SN,LME,NA
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LME Zinc,ZS,LME,NA
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Iron Ore,TIO,SGX,NA
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Rubber,TSR,SGX,NA
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