Here's a more complete version of the pipeline_manager.py file. Note that the actual implementations for each step are not provided, as they are specific to your data and API. You'll need to fill in the logic for each step according to your requirements.
import os
import datetime
class PipelineManager:
def __init__(self, workspace):
self.workspace = workspace
def create_pipeline(self, name=None, date_range=None):
if name is None:
name = f"Pipeline_{datetime.datetime.now().strftime('%Y%m%d_%H%M%S')}"
pipeline_folder = os.path.join(self.workspace, name)
os.makedirs(pipeline_folder, exist_ok=True)
return Pipeline(pipeline_folder, date_range)
class Pipeline:
def __init__(self, folder, date_range):
self.folder = folder
self.date_range = date_range
self.finished_steps = set()
def step_1_download_raw_data(self):
# Implement your logic for downloading raw data files from API
output_folder = os.path.join(self.folder, "raw_data")
os.makedirs(output_folder, exist_ok=True)
# Save raw data to output_folder
self.finished_steps.add(1)
def step_2_process_raw_data(self):
# Implement your logic for processing raw data by extracting order tags
output_folder = os.path.join(self.folder, "processed_data")
os.makedirs(output_folder, exist_ok=True)
# Save processed data to output_folder
self.finished_steps.add(2)
def step_3_retrieve_order_details(self):
# Implement your logic for retrieving order details from a database
output_folder = os.path.join(self.folder, "order_details")
os.makedirs(output_folder, exist_ok=True)
# Save order details to output_folder
self.finished_steps.add(3)
def step_4_extract_metadata(self):
# Implement your logic for extracting metadata from the datasets
output_folder = os.path.join(self.folder, "metadata")
os.makedirs(output_folder, exist_ok=True)
# Save metadata to output_folder
self.finished_steps.add(4)
def step_5_enrich_processed_data(self):
# Implement your logic for enriching processed data using metadata
output_folder = os.path.join(self.folder, "enriched_data")
os.makedirs(output_folder, exist_ok=True)
# Save enriched data to output_folder
self.finished_steps.add(5)
def step_6_generate_allocation_events(self):
# Implement your logic for generating allocation events
output_folder = os.path.join(self.folder, "allocation_events")
os.makedirs(output_folder, exist_ok=True)
# Save allocation events to output_folder
self.finished_steps.add(6)
def step_7_generate_constituent_orders(self):
# Implement your logic for generating constituent orders
output_folder = os.path.join(self.folder, "constituent_orders")
os.makedirs(output_folder, exist_ok=True)
# Save constituent orders to output_folder
self.finished_steps.add(7)
def step_8_generate_reference_data(self):
# Implement your logic for generating reference data
output_folder = os.path.join(self.folder, "reference_data")
os.makedirs(output_folder, exist_ok=True)
# Save reference data to output_folder
self.finished_steps.add(8)
def step_9_repackage_files(self):
# Implement your logic for repackaging the files into a finished folder
output_folder = os.path.join(self.folder, "finished")
os.makedirs(output_folder, exist_ok=True)
# Save repackaged files to output_folder
self.finished_steps.add(9)
def run_all_steps(self):
self.step_1_download_raw_data()
self.step_2_process_raw_data()
self.step_3_retrieve_order_details()
self.step_4_extract_metadata()
self.step_5_enrich_processed_data()
self.step_6_generate_allocation_events()
self.step_7_generate_constituent_orders()
self.step_8_generate_reference_data()
self.step_9_repackage_files()
This implementation includes individual methods for each step in the pipeline, as well as a run_all_steps method that runs all the steps in sequence. The finished_steps set is used to keep track of which steps have been completed for each pipeline.
Remember to replace the placeholder comments with your actual implementation for each step.
Here's a more complete version of the
pipeline_manager.pyfile. Note that the actual implementations for each step are not provided, as they are specific to your data and API. You'll need to fill in the logic for each step according to your requirements.This implementation includes individual methods for each step in the pipeline, as well as a
run_all_stepsmethod that runs all the steps in sequence. Thefinished_stepsset is used to keep track of which steps have been completed for each pipeline.Remember to replace the placeholder comments with your actual implementation for each step.