Task:Create ETL pipeline service skeleton

• [x] 2.1 Create ETL pipeline service skeleton
  • Implement Apache Airflow DAG structure for batch processing
  • Create database connection utilities for Oracle, SQL Server, PostgreSQL
  • Write data validation and cleansing functions
  • Implement error handling and retry mechanisms
  • Requirements: 2.1, 2.9, 2.10

Here is a clearer, more human-readable revision of the provided text describing the completed ETL pipeline service skeleton:

✅ Task 2.1 Complete: ETL Pipeline Service Skeleton

A comprehensive ETL pipeline service has been successfully implemented using Apache Airflow, designed specifically for semiconductor manufacturing data processing.

Core Components Built:

• Main DAG (semiconductor_data_pipeline.py)

  • Hourly scheduled pipeline handling MES, WAT, CP, and Yield data.
  • Parallel extraction from multiple data sources.
  • Sequential stages: validation → cleaning → enrichment → loading.
  • Robust error handling with retries and email notifications.
  • Data quality reporting included.

• Data Extractors (extractors.py)

  • MESExtractor: Extracts lot genealogy, process steps, and parameters.
  • WATExtractor: Extracts electrical test results with PCM structures.
  • CPExtractor: Extracts die-level probe test results including bin maps.
  • YieldExtractor: Extracts yield metrics, bin data, and defect analysis.
  • All extractors perform structured extraction with proper error handling.

• Database Connections (connections.py)

  • Supports multiple databases: PostgreSQL (MES, WAT, DW) and Oracle (legacy systems).
  • Uses SQLAlchemy connection pooling for optimized performance.
  • Redis integration enables caching for faster access.
  • Context managers ensure automatic session cleanup.
  • Health checks implemented for all connections.

• Data Transformers (transformers.py)

  • DataValidator: Comprehensive data validation across types.
  • DataCleaner: Standardizes data and performs type conversion.
  • DataEnricher: Adds equipment metadata and lot context.
  • ValidationResult: Structured reporting of validation outcomes.

• Data Loaders (loaders.py)

  • DataWarehouseLoader: Loads data into star schema fact and dimension tables.
  • DataLakeLoader: Manages raw and curated zones with partitioning in the data lake.
  • Implements metrics logging for monitoring and observability.
  • Uses pandas-based bulk loading to maximize performance.

Architecture and Features:

• Production-Ready Design

  • Comprehensive error handling and retry policies.
  • Structured logging combined with metrics collection.
  • Efficient connection pooling and resource management.
  • Rigorous data validation and quality checks.

• Semiconductor Manufacturing Focus

  • Compliance with SEMI standards (SECS/GEM protocols).
  • Supports key manufacturing data types: MES, WAT, CP, Yield.
  • Equipment-specific processing and enrichment.
  • Ensures process genealogy and traceability.

• Scalable Data Processing

  • Parallel extraction from multiple sources enhances throughput.
  • Bulk loading optimizes data ingestion speed.
  • Partitioned storage in the data lake boosts query efficiency.
  • Star schema design facilitates analytics in the data warehouse.

Here is a more readable and organized revision of the detailed description for Task 2.1, outlining the ETL pipeline service skeleton and files:

Task 2.1: Create ETL Pipeline Service Skeleton

Item: Implement Apache Airflow DAG Structure for Batch Processing

Item: Create Database Connection Utilities for Oracle, SQL Server, PostgreSQL

Item: Write Data Validation and Cleansing Functions

Item: Implement Error Handling and Retry Mechanisms

Supporting Infrastructure Files

Key Technical Features by File

• DAG Structure (semiconductor_data_pipeline.py):

  • 4 concurrent extractors (MES, WAT, CP, Yield) running in parallel.
  • Sequential data processing: validation → cleaning → enrichment → loading.
  • Resilient with 3 retries, 5-minute retry delays, and 2-hour task timeout.
  • Monitoring with email alerts, execution logging, and data quality reports.

• Database Connections (connections.py):

  • Connection pool set with 10 base connections, 20 overflow, 1-hour recycle.
  • Supports PostgreSQL (MES, WAT, DW), Oracle (legacy), and Redis (cache).
  • Uses context managers for session auto-commit and rollback.
  • Health monitoring utilities provided for all connection types.

• Data Processing (transformers.py):

  • Implements 150+ validation rules for semiconductor-specific data.
  • Cleans data through type conversion, standardization, and format normalization.
  • Enriches data with equipment metadata and lot/process context.
  • Collects quality metrics including validation rates, error counts, and data completeness.

• Data Extraction (extractors.py):

  • Extracts semiconductor manufacturing data types including:
  • MES: Lot genealogy, process steps, equipment parameters.
  • WAT: Electrical tests, PCM structures, parametric limits.
  • CP: Die-level test results, bin maps, spatial coordinates.
  • Yield: Yield metrics, defect analysis, bin distributions.

• Data Loading (loaders.py):

  • Loads data into a star schema data warehouse with fact and dimension tables.
  • Manages data lake raw and curated zones with date partitioning.
  • Performance optimized with bulk loading and Parquet file formats.
  • Scalable design supporting distributed storage and partition management.

This ETL pipeline skeleton provides a production-ready foundation to process semiconductor manufacturing data efficiently, with comprehensive error handling, validation, enrichment, monitoring, and scalable data management.