Design Document:ETL Pipeline Service

• Technology: Apache Airflow, Apache Spark
• Purpose: Batch processing of historical data from MES, WAT, CP, and Yield systems
• Interfaces:
  • SEMI SECS/GEM protocol adapters
  • REST API connectors
  • Database connectors (Oracle, SQL Server, PostgreSQL)
• Data Processing: Data validation, cleansing, transformation, and enrichment
• Scheduling: Configurable batch schedules with dependency management

Enhanced ETL Pipeline Design & Implementation

Based on requirement for an ETL pipeline, here is a more embodied design and implementation plan using the specified technologies. This design focuses on creating a robust, scalable, and maintainable system for batch processing of historical semiconductor manufacturing data.

1. Data Ingestion Service

Service: Airflow DAGs for Data Ingestion

• Objective: To pull data from various sources (MES, WAT, CP, Yield) and land it in a raw data lake.
• Technology Stack:
  • Orchestration: Apache Airflow as the primary scheduler.
  • Connectors: Python libraries (e.g., sqlalchemy for databases, custom requests for REST APIs). For SECS/GEM, a dedicated Python wrapper around a SECS/GEM client library.
  • Storage: An on-premises MinIO or S3-compatible object store, acting as the raw data lake.
• Implementation Details:
  • Dynamic DAG Generation: Instead of manually creating a DAG for each data source, use a Python script to dynamically generate DAGs from a configuration file (e.g., YAML). This makes it easy to add new data sources without code changes. The config file would define the source type, connection details, and ingestion frequency.
  • Error Handling: Each task in the DAG will have retries and alerting configured. A failure in one source's ingestion will not halt the entire pipeline.
  • Data Landing: Data will be landed in the raw data lake in its original format (e.g., CSV, JSON, XML). The directory structure will be partitioned by source and date: /raw/{source_system}/{table_name}/year=YYYY/month=MM/day=DD/. This structure is crucial for efficient data lake queries.

2. Data Processing & Transformation Service

Service: Spark for Data Cleansing & Transformation

• Objective: To take the raw data, validate it, cleanse it, and transform it into a structured, query-optimized format.
• Technology Stack:
  • Processing: Apache Spark running on Kubernetes (Spark on K8s). This allows for distributed processing of large datasets.
  • Language: PySpark for writing transformation logic.
  • Storage: The cleansed data will be stored in the data warehouse (ClickHouse or Apache Pinot) in a columnar format (e.g., Parquet).
• Implementation Details:
  • Data Validation: Use Spark to implement data quality checks. This includes schema validation (e.g., are the expected columns present?), data type checks, and range validation (e.g., is the temperature reading within a valid range?). Bad data is quarantined in a separate directory (/quarantine/).
  • Data Cleansing & Enrichment:
    • Missing Data Imputation: Use statistical methods (e.g., mean, median) or machine learning models to fill in missing sensor readings or process parameters.
    • Outlier Detection: Identify and flag or remove extreme outliers using statistical methods like Z-score or more advanced techniques like Isolation Forests.
    • Normalization: Standardize terminology across different systems (e.g., ensure lot_id from MES matches lot_id from the CP system).
    • Enrichment: Join data from different sources. For example, enrich WAT data with the corresponding MES lot history and FDC sensor data for that lot. This creates a unified "golden record" for traceability.
  • Partitioning: Partition the transformed data in the data warehouse based on common query patterns (e.g., date, product_id, process_step) to speed up analysis. This is a core part of the design for Requirement 6 (Data Traceability).

3. Workflow Automation and Monitoring

Service: Airflow for Workflow & Dependency Management

• Objective: To automate the entire ETL pipeline, from ingestion to final data loading.
• Implementation Details:
  • Workflow as a DAG: Create a main Airflow DAG that orchestrates the entire process.
    1. ingest_mes_data_task
    2. ingest_wat_data_task
    3. ingest_cp_data_task
    4. ...
    5. transform_mes_wat_data_task (This task is dependent on the completion of ingest_mes_data_task and ingest_wat_data_task).
    6. load_to_data_warehouse_task
  • Scheduling: Define a configurable schedule (e.g., daily at 1 AM). For critical data, use a more frequent schedule.
  • Monitoring: Airflow's built-in UI provides a graphical representation of the pipeline, showing task status, run times, and failures. Integrate Airflow alerts with your monitoring system (Prometheus/Grafana).

This design provides a clear separation of concerns, with Airflow handling orchestration and Spark handling the heavy lifting of data processing. This modularity makes the system easy to debug, scale, and maintain, directly addressing the need for a professional and workable solution.