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02_Source_To_Bronze.py
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02_Source_To_Bronze.py
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# Databricks notebook source
# MAGIC %md
# MAGIC
# MAGIC ### TRANSFERING DATA FROM SOURCE TO BRONZE
# MAGIC
# MAGIC
# MAGIC Extracting the data from Oracle DB (i.e. SOURCE) and Populating it to the Bronze Table (i.e. IntercambioBronzeDB)
# COMMAND ----------
# MAGIC %md
# MAGIC ### INCULDING DIFFERENT NOTEBOOKS
# COMMAND ----------
# MAGIC %md
# MAGIC Including Notebook Named **TableAndDataMappingConfigs**
# COMMAND ----------
# MAGIC %run "./customConfig/TableAndDataMappingConfigs"
# COMMAND ----------
# MAGIC %md
# MAGIC Including Notebook Named **0_DefaultTablesCreation**
# COMMAND ----------
# MAGIC %run "./0_DefaultTablesCreation"
# COMMAND ----------
# MAGIC %md
# MAGIC Including Notebook Named **01_CommonFunctions**
# COMMAND ----------
# MAGIC %run "./01_CommonFunctions"
# COMMAND ----------
# MAGIC %md
# MAGIC ### DOWNLOADING REQUIRED MODULES FOR THE ACTION
# COMMAND ----------
# MAGIC %sh
# MAGIC python -m pip install --upgrade pip
# MAGIC python -m pip install oracledb
# COMMAND ----------
# MAGIC %md
# MAGIC ### IMPORTANT CONNECTIONS DETAIL VARIABLES
# COMMAND ----------
user = ConnectionINFO["user"]
password = ConnectionINFO["password"]
dsn = ConnectionINFO["dsn"]
# COMMAND ----------
# MAGIC %md
# MAGIC ### DEFAULT VARIABLES
# COMMAND ----------
isLegacyIngestion = Legacy["STATUS"]
end_date = None
initial_start_date = datetime(year=2023,month=1,day=1)
cardNumberIngestion = cardNumber["STATUS"]
# COMMAND ----------
# MAGIC %md
# MAGIC ### EXTRACTION FROM SOURCE DATA
# COMMAND ----------
print(user)
# COMMAND ----------
import pandas as pd
from datetime import datetime
import oracledb
from pyspark.sql.functions import col,lit
try:
connection = oracledb.connect(user = user,password = password,dsn = dsn)
fetcher = connection.cursor()
connection.commit()
if isLegacyIngestion:
tableName = legacyIngetionStatus
else:
tableName = ingetionStatus
bronzeTableName = bronzeTable
if(isLegacyIngestion):
startDate = getLastUpdateDateLegacy(bronzeDataBase,tableName,"queryEndDate")
start_date_str = start_date.strftime("%d-%b-%Y")
else:
try:
spark.sql("SELECT * FROM InterCambio_DataIngetion_Status").collect()[0][0]
startDate = getLastUpdateDate(bronzeDataBase,tableName,"queryEndDate")
start_date_str = start_date.strftime("%d-%b-%Y")
except:
print("except")
startDate = initial_start_date
start_date_str = startDate.strftime("%d-%b-%Y")
print("Start Date is ",start_date_str)
if(isLegacyIngestion):
end_date = start_date + timedelta(hours=8)
end_date_str = end_date.strftime("%d-%b-%Y")
else:
end_date = datetime.now()
end_date_str = end_date.strftime("%d-%b-%Y")
print("start_date_str is ",start_date_str)
print("end_date_str is ",end_date_str)
if cardNumberIngestion:
query = '''Select * From dw_ods.ODS_RES_A500_INTERCAMBIO WHERE COD_BENEFICIARIO in ({0}) or COD_BENEFICIARIO in ({1}) ORDER BY COD_BENEFICIARIO'''.format(str(cardNumberList1)[1:-1],str(cardNumberList2)[1:-1])
elif isLegacyIngestion:
query = '''Select * From dw_ods.ODS_RES_A500_INTERCAMBIO WHERE DT_ALTERACAO >= '{0}' and DT_ALTERACAO <= '{1}' ORDER BY DT_ALTERACAO'''.format(start_date_str,end_date_str)
else:
query = '''Select "DT_CARGA",
"DT_ALTERACAO",
"ID_TRANSACAO",
"DT_TRANSACAO",
"ID_STATUS_TRANSACAO",
"STATUS",
"CD_UNIMED_ORIGEM",
"UNIMED_ORIGEM",
"CD_UNIMED_DESTINO",
"UNIMED_DESTINO",
"CD_UNIMED_BENEFICIARIO",
"ID_BENEFICIARIO",
"COD_BENEFICIARIO",
"FG_RECEM_NATO",
"TIPO_PACIENTE",
"NOME_CONTRATADO_EXECUTANTE",
"CNES_CONTRATADO_EXECUTANTE",
"CNPJ_CONTRATADO_EXECUTANTE",
"TIPO_PRESTADOR",
"FG_RECURSO_PROPRIO",
"NOME_PROFISSIONAL_EXECUTANTE",
"SG_CONSELHO_PROFISSIONAL_EXECUTANTE",
"NR_CONSELHO_PROFISSIONAL_EXECUTANTE",
"SG_UF_PROFISSIONAL_EXECUTANTE",
"CD_CBO_PROFISSIONAL_EXECUTANTE",
"TIPO_CONSULTA",
"TIPO_ACIDENTE",
"NOME_PROFISSIONAL_SOLICITANTE",
"SG_CONSELHO_PROFISSIONAL_SOLICITANTE",
"NR_CONSELHO_PROFISSIONAL_SOLICITANTE",
"SG_UF_PROFISSIONAL_SOLICITANTE",
"CD_CBO_PROFISSIONAL_SOLICITANTE",
"CD_TECNICA_UTILIZADA",
"TIPO_PARTICIPACAO",
"TIPO_ATENDIMENTO",
"CD_CARATER_ATENDIMENTO",
"TIPO_ENCERRAMENTO",
"DT_EXECUCAO",
"DT_ATENDIMENTO",
"NR_SEQ_ITEM",
"CD_ITEM_UNICO",
"TIPO_TABELA",
"CD_SERVICO",
"TP_GUIA",
"TIPO_INTERNACAO,TIPO_REGIME_INTERNACAO,CD_CID,NR_GUIATISSPRESTADOR,CHAVE_NOTA From dw_ods.ODS_RES_A500_INTERCAMBIO WHERE DT_CARGA >= '{0}' and DT_CARGA <= '{1}' ORDER BY DT_CARGA'''.format(start_date_str,end_date_str)
print("query : "+query)
fetcher.execute(query)
result = fetcher.fetchall()
print(result[0])
except Exception as e:
print("Exception Occured : ",e)
updateDataIngestionStats(bronzeDataBase,tableName,startDate, end_date, len(result), str(e))
dbutils.notebook.exit(e)
finally:
connection.close()
# COMMAND ----------
# MAGIC %md
# MAGIC ### MERGE SOURCE DATA IN BRONZE DATABASE
# COMMAND ----------
import pandas as pd
sourceTable = "Intecambio_view"
if len(result) > 0:
if spark._jsparkSession.catalog().tableExists(bronzeDataBase, bronzeTableName):
intercambio_df = pd.DataFrame(
result,
columns=[
"DT_CARGA",
"DT_ALTERACAO",
"ID_TRANSACAO",
"DT_TRANSACAO",
"ID_STATUS_TRANSACAO",
"STATUS",
"CD_UNIMED_ORIGEM",
"UNIMED_ORIGEM",
"CD_UNIMED_DESTINO",
"UNIMED_DESTINO",
"CD_UNIMED_BENEFICIARIO",
"ID_BENEFICIARIO",
"COD_BENEFICIARIO",
"FG_RECEM_NATO",
"TIPO_PACIENTE",
"NOME_CONTRATADO_EXECUTANTE",
"CNES_CONTRATADO_EXECUTANTE",
"CNPJ_CONTRATADO_EXECUTANTE",
"TIPO_PRESTADOR",
"FG_RECURSO_PROPRIO",
"NOME_PROFISSIONAL_EXECUTANTE",
"SG_CONSELHO_PROFISSIONAL_EXECUTANTE",
"NR_CONSELHO_PROFISSIONAL_EXECUTANTE",
"SG_UF_PROFISSIONAL_EXECUTANTE",
"CD_CBO_PROFISSIONAL_EXECUTANTE",
"TIPO_CONSULTA",
"TIPO_ACIDENTE",
"NOME_PROFISSIONAL_SOLICITANTE",
"SG_CONSELHO_PROFISSIONAL_SOLICITANTE",
"NR_CONSELHO_PROFISSIONAL_SOLICITANTE",
"SG_UF_PROFISSIONAL_SOLICITANTE",
"CD_CBO_PROFISSIONAL_SOLICITANTE",
"CD_TECNICA_UTILIZADA",
"TIPO_PARTICIPACAO",
"TIPO_ATENDIMENTO",
"CD_CARATER_ATENDIMENTO",
"TIPO_ENCERRAMENTO",
"DT_EXECUCAO",
"DT_ATENDIMENTO",
"NR_SEQ_ITEM",
"CD_ITEM_UNICO",
"TIPO_TABELA",
"CD_SERVICO",
"TP_GUIA",
"TIPO_INTERNACAO",
"TIPO_REGIME_INTERNACAO",
"CD_CID",
"NR_GUIATISSPRESTADOR",
"CHAVE_NOTA",
],
)
display(intercambio_df)
intercambio_sparkDF = spark.createDataFrame(intercambio_df)
intercambio_sparkDF.createOrReplaceTempView(sourceTable)
upsertSourceToBronze(sourceTable, bronzeTableName, "DT_CARGA", "DT_ALTERACAO")
else:
updateDataIngestionStats(
bronzeDataBase,
tableName,
startDate,
end_date,
len(result),
"BRONZE TABLE NOT CREATED",
)
dbutils.notebook.exit("Bronze tables not created!")
else:
print(tableName)
updateDataIngestionStats(
bronzeDataBase,
tableName,
startDate,
end_date,
len(result),
"THERE IS NO DATA IN BRONZE TABLE",
)
# COMMAND ----------
# MAGIC %sql
# MAGIC select count(*) from IntercambioBronze