added facts

main.py

@@ -6,6 +6,7 @@ import yaml
 from pipeline.ingest import Ingest
 from pipeline.raw_to_base import RawToBase
 from pipeline.dimensions import DimAccounts, DimCategories, DimPayees, DimDate
+from pipeline.facts import FactTransactions, FactScheduledTransactions
 
 dotenv.load_dotenv()
 
@@ -26,3 +27,5 @@ if __name__ == '__main__':
     DimCategories(config)
     DimPayees(config)
     DimDate(config)
+    FactTransactions(config)
+    FactScheduledTransactions(config)

pipeline/facts.py

@@ -0,0 +1,103 @@
+import polars as pl
+import logging
+import os
+from datetime import date
+
+class Facts:
+    def __init__(self, config):
+        self.config = config
+        self.base_file_path = self.config['base_data_path']
+        os.makedirs(self.config['warehouse_data_path'], exist_ok=True)
+        
+    def get_full_file_path(self, file_name):
+        return f"{self.base_file_path}/{file_name}"
+    
+class FactTransactions(Facts):
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.file_path = self.get_full_file_path('transactions.parquet')
+        self.transform()
+
+    def transform(self):
+        # Read the parquet file into a polars DataFrame
+        transactions_df = pl.read_parquet(self.file_path)
+
+        # Transform the DataFrame
+        logging.info("Transforming the transactions DataFrame")
+        transactions_df = (
+            transactions_df
+            .with_columns([
+                pl.col("id").alias("transaction_id"),
+                pl.col("date").alias("transaction_date"),
+                pl.col("amount").alias("transaction_amount"),
+                pl.col("memo").alias("transaction_memo"),
+                pl.col("cleared").alias("transaction_cleared"),
+                pl.col("approved").alias("transaction_approved"),
+                pl.col("flag_color").alias("transaction_flag_color"),
+                pl.col("account_id").alias("account_id"),
+                pl.col("payee_id").alias("payee_id"),
+                pl.col("category_id").alias("category_id"),
+                pl.col("transfer_account_id").alias("transfer_account_id"),
+            ])
+            .with_columns([
+                pl.col("memo").fill_null("unknown"),
+                (pl.col("amount") / 100).alias("transaction_amount"),
+            ])
+            .drop([
+                "transfer_transaction_id", "matched_transaction_id", "import_id",
+                "subtransactions", "deleted","flag_name","account_name",
+                "payee_name","category_name","import_payee_name","import_payee_name_original",
+                "debt_transaction_type","ingestion_date"
+            ])
+        )
+
+        # Write the DataFrame to a new parquet file
+        logging.info("Writing the transformed transactions DataFrame to parquet file")
+        transactions_df.write_parquet(self.config['warehouse_data_path'] + '/transactions.parquet')
+
+class FactScheduledTransactions(Facts):
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.file_path = self.get_full_file_path('scheduled_transactions.parquet')
+        self.transform()
+
+    def transform(self):
+        # Read the parquet file into a polars DataFrame
+        try:
+            scheduled_transactions_df = pl.read_parquet(self.file_path)
+        except FileNotFoundError:
+            logging.error("The scheduled transactions DataFrame does not exist")
+            return
+
+        # Transform the DataFrame
+        logging.info("Transforming the scheduled transactions DataFrame")
+        scheduled_transactions_df = (
+            scheduled_transactions_df
+            .with_columns([
+                pl.col("id").alias("scheduled_transaction_id"),
+                pl.col("date").alias("scheduled_transaction_date"),
+                pl.col("amount").alias("scheduled_transaction_amount"),
+                pl.col("memo").alias("scheduled_transaction_memo"),
+                pl.col("flag_color").alias("scheduled_transaction_flag_color"),
+                pl.col("account_id").alias("account_id"),
+                pl.col("payee_id").alias("payee_id"),
+                pl.col("category_id").alias("category_id"),
+                pl.col("transfer_account_id").alias("transfer_account_id"),
+            ])
+            .with_columns([
+                pl.col("memo").fill_null("unknown"),
+                (pl.col("amount") / 100).alias("scheduled_transaction_amount"),
+            ])
+            .drop([
+                "transfer_transaction_id", "matched_transaction_id", "import_id",
+                "subtransactions", "deleted","flag_name","account_name",
+                "payee_name","category_name","import_payee_name","import_payee_name_original",
+                "debt_transaction_type","ingestion_date"
+            ])
+        )
+
+        # Write the DataFrame to a new parquet file
+        logging.info("Writing the transformed scheduled transactions DataFrame to parquet file")
+        scheduled_transactions_df.write_parquet(self.config['warehouse_data_path'] + '/scheduled_transactions.parquet')

test.py

@@ -1,7 +1,7 @@
 import polars as pl
 
 #entities = ['accounts', 'categories', 'months', 'payees', 'transactions', 'scheduled_transactions']
-entities = ['payees']
+entities = ['transactions']
 
 
 for entity in entities: