Task:Create ETL pipeline service skeleton

This content originally appeared on DEV Community and was authored by Y.C Lee

• [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.

This content originally appeared on DEV Community and was authored by Y.C Lee