Created
June 26, 2020 19:13
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import sys | |
from awsglue.utils import getResolvedOptions | |
from pyspark.context import SparkContext | |
from awsglue.context import GlueContext | |
from awsglue.dynamicframe import DynamicFrame | |
from awsglue.job import Job | |
from pyspark.sql.functions import udf | |
from pyspark.sql.types import StringType | |
glueContext = GlueContext(SparkContext.getOrCreate()) | |
# Data Catalog: database and table name | |
db_name = "db1" | |
tbl_name = "sales" | |
# S3 location for output | |
# Swap "<yourbucket>" with the name of your bucket | |
output_dir = "s3://079620682254virginianewlakeagain/clean_sales" | |
# Read data into a DynamicFrame using the Data Catalog metadata | |
sales_dyf = glueContext.create_dynamic_frame.from_catalog(database = db_name, table_name = tbl_name) | |
sales_dataframe = sales_dyf.toDF() | |
# udf returns the last 4 digits of the card and deals with variance in numeric spacing by removing whitespace | |
@udf(returnType=StringType()) | |
def remove_currency(price): | |
price = price.replace("$", "") | |
return price | |
final_dataframe = sales_dataframe.withColumn("price", remove_currency(sales_dataframe["price"])) | |
clean_tmp_dyf = DynamicFrame.fromDF(final_dataframe, glueContext, "clean") | |
final_dyf = clean_tmp_dyf.apply_mapping([('card_id', 'bigint', 'card_id', 'bigint'), | |
('customer_id', 'bigint', 'customer_id', 'bigint'), | |
('provider name', 'string', 'provider.name', 'string'), | |
('product_id', 'bigint', 'product_id', 'bigint'), | |
('price', 'string','price', 'decimal')]); | |
final_dyf = final_dyf.drop_fields(['provider']) | |
final_dyf.printSchema(); | |
glueContext.write_dynamic_frame.from_options( | |
frame = final_dyf, | |
connection_type = "s3", | |
connection_options = {"path": output_dir}, | |
format = "csv") |
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