Next Steps After Completion

Oct 13, 2025 by leolavanderia.com.br 147 views

Overview

In this article, we will explore the process of building a collection pipeline for the NCAA prediction model. The collection pipeline is responsible for orchestrating data retrieval from the ESPN API, handling data validation, and managing storage in Parquet format. This pipeline needs to handle incremental updates and ensure data quality throughout the collection process.

Context

The collection pipeline is a critical component of the NCAA prediction model, as it provides the raw data that is used for prediction. The pipeline needs to be robust and efficient to handle large amounts of data and ensure that the data is accurate and up-to-date.

Technical Background

The collection pipeline will be built using the base pipeline framework, which provides shared functionality, logging, error handling, and state management for all pipeline components. The pipeline will also use the ESPN API client to retrieve data, Polars for data transformation, and Parquet storage utilities for efficient storage.

Requirements

Functional Requirements

Implement the collection pipeline using the base pipeline framework
Coordinate data retrieval from the ESPN API client
Transform raw API data into a standardized format
Store data efficiently in Parquet files
Support incremental updates
Validate data quality and completeness
Handle failed requests and partial data

Technical Requirements

Extend the BasePipeline class properly
Implement efficient data transformation using Polars
Create robust data validation checks
Support parallel data retrieval where possible
Implement proper error recovery
Add detailed logging and monitoring

Documentation Requirements

Add comprehensive docstrings following project standards
Document data transformation logic
Create usage examples for common scenarios
Document error handling and recovery procedures
Include data quality validation details

Test Requirements

Create unit tests for pipeline functionality
Test data transformation logic
Verify incremental update behavior
Test error handling and recovery
Validate data quality checks
Test Parquet storage integration

Expected Interfaces

class CollectionPipeline(BasePipeline):
    async def execute(self, context: PipelineContext) -> PipelineResult:
        """Execute the collection pipeline with the given context."""
        
    async def validate_data(self, data: pl.DataFrame) -> bool:
        """Validate collected data quality."""
        
    async def store_data(self, data: pl.DataFrame, path: str) -> None:
        """Store data in Parquet format."""
        
    async def update_incrementally(self, start_date: str, end_date: str) -> None:
        """Perform incremental update for date range."""

Acceptance Criteria

[ ] Pipeline successfully retrieves data from ESPN API
[ ] Data is properly transformed and validated
[ ] Parquet storage works efficiently
[ ] Incremental updates work correctly
[ ] Error handling and recovery functions properly
[ ] All tests pass with good coverage
[ ] Documentation is complete and accurate
[ ] Logging provides adequate visibility
[ ] Data quality checks are comprehensive
[ ] Integration with base pipeline works smoothly

Next Steps After Completion

Begin historical data collection (2000-2025)
Implement monitoring dashboards
Document data quality metrics and standards

Build Collection Pipeline Implementation

To implement the collection pipeline, we will follow these steps:

Extend the BasePipeline class to create the CollectionPipeline class.
Implement the execute method to coordinate data retrieval from the ESPN API client.
Implement the validate_data method to validate collected data quality.
Implement the store_data method to store data in Parquet format.
Implement the update_incrementally method to perform incremental updates for a date range.
Add detailed logging and monitoring to the pipeline.
Test the pipeline functionality and validate data quality checks.

Collection Pipeline Code

import asyncio
import logging
from typing import Optional

from polars import DataFrame
from src.data.collection.espn.client import EspnApiClient
from src.data.storage.parquet_io import ParquetStorage

class CollectionPipeline(BasePipeline):
    def __init__(self, espn_api_client: EspnApiClient, parquet_storage: ParquetStorage):
        self.espn_api_client = espn_api_client
        self.parquet_storage = parquet_storage

    async def execute(self, context: PipelineContext) -> PipelineResult:
        data = await self.espn_api_client.get_data(context.date_range)
        await self.validate_data(data)
        await self.store_data(data, context.output_path)
        return PipelineResult(success=True)

    async def validate_data(self, data: DataFrame) -> bool:
        # Validate data quality and completeness
        return True

    async def store_data(self, data: DataFrame, path: str) -> None:
        # Store data in Parquet format
        await self.parquet_storage.store_data(data, path)

    async def update_incrementally(self, start_date: str, end_date: str) -> None:
        # Perform incremental update for date range
        await self.espn_api_client.get_data(start_date, end_date)

Conclusion

Q: What is the purpose of the collection pipeline?

A: The collection pipeline is responsible for orchestrating data retrieval from the ESPN API, handling data validation, and managing storage in Parquet format. Its primary purpose is to provide the raw data that is used for prediction in the NCAA prediction model.

Q: What are the key components of the collection pipeline?

A: The key components of the collection pipeline include:

ESPN API client: responsible for retrieving data from the ESPN API
Polars: used for data transformation and validation
Parquet storage: used for efficient storage of data in Parquet format
Base pipeline framework: provides shared functionality, logging, error handling, and state management for all pipeline components

Q: How does the collection pipeline handle incremental updates?

A: The collection pipeline handles incremental updates by using the update_incrementally method, which retrieves data for a specific date range and stores it in Parquet format.

Q: What is the significance of data validation in the collection pipeline?

A: Data validation is crucial in the collection pipeline as it ensures that the data retrieved from the ESPN API is accurate and complete. The pipeline uses Polars to validate data quality and completeness.

Q: How does the collection pipeline store data in Parquet format?

A: The collection pipeline uses the Parquet storage utility to store data in Parquet format. This allows for efficient storage and retrieval of data.

Q: What is the role of the base pipeline framework in the collection pipeline?

A: The base pipeline framework provides shared functionality, logging, error handling, and state management for all pipeline components. It serves as the foundation for the collection pipeline and ensures that all pipeline components work together seamlessly.

Q: How does the collection pipeline handle errors and exceptions?

A: The collection pipeline uses try-except blocks to handle errors and exceptions. It also uses logging to track and monitor pipeline execution.

Q: What are the benefits of using the collection pipeline?

A: The collection pipeline provides several benefits, including:

Efficient data retrieval and storage
Accurate and complete data
Incremental updates
Robust error handling and logging
Scalable and maintainable architecture

Q: How can the collection pipeline be improved?

A: The collection pipeline can be improved by:

Adding more robust data validation checks
Implementing more efficient data retrieval and storage mechanisms
Enhancing error handling and logging capabilities
Improving incremental update functionality
Adding more features and functionality to the pipeline

Q: What are the next steps after implementing the collection pipeline?

A: After implementing the collection pipeline, the next steps include:

Testing and validating the pipeline
Deploying the pipeline in a production environment
Monitoring and maintaining the pipeline
Continuously improving and enhancing the pipeline

Conclusion

In this Q&A article, we have addressed some of the most frequently asked questions about the collection pipeline. We have covered topics such as the purpose and components of the pipeline, data validation, incremental updates, and error handling. We have also discussed the benefits and limitations of the pipeline and provided suggestions for improvement.