To build a dynamic automated process where the steps (queries or DataFrame operations) are read from a table, so you can design a flexible framework

To standardize the process of updating the metadata table and include version control, we can implement the following:

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1. Metadata Management Approach

1. Version Control in Metadata Table:
   • Add a version column to track changes to queries or logic.
   • Use a status column to differentiate between active and inactive records.
   • Introduce created_at and updated_at timestamps for auditing purposes.
2. Standardized Update Process:
   • Use a template or form for metadata updates.
   • Maintain a change log to document changes in logic and their rationale.
3. Review & Approval Workflow:
   • Require changes to metadata to go through a review process before activation.
   • Store updates in a staging table before moving them to the active metadata table.
4. Version History:
   • Maintain all previous versions of the metadata in the same table (using version and status).
   • Allow rollback to previous versions if needed.

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2. Updated Metadata Table Schema

CREATE TABLE IF NOT EXISTS metadatatable (
    program_name STRING,
    product_name STRING,
    stage_name STRING,
    step_name STRING,
    version INT DEFAULT 1, -- Version number
    status STRING DEFAULT 'active', -- active or inactive
    operation_type STRING, -- SQL, DataFrame
    query TEXT,
    custom_logic TEXT,
    temp_view_name STRING,
    table_name STRING,
    write_mode STRING, -- overwrite, append, archive, snapshot
    snapshot_mode STRING,
    stage_priority INT,
    steps_priority INT,
    month_year STRING,
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

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3. Version Control Logic

a) Adding a New Query

When adding a new step or query:

1. Increment Version: Ensure the version for the new step is 1.
2. Set Status to Active: Mark the new step as active.

Example:

INSERT INTO metadatatable (
    program_name, product_name, stage_name, step_name, version, status, operation_type, query, 
    custom_logic, temp_view_name, table_name, write_mode, snapshot_mode, stage_priority, 
    steps_priority, month_year, created_at, updated_at
)
VALUES (
    'Risk Program', 'Personal Loan', 'Stage 1', 'New Logic Step', 1, 'active', 'SQL', 
    'SELECT * FROM transactions WHERE risk_score > 700', 
    NULL, 'filtered_view', 'high_risk_table', 'overwrite', NULL, 1, 3, '202412', CURRENT_TIMESTAMP, CURRENT_TIMESTAMP
);

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b) Modifying an Existing Query

When updating an existing step:

1. Set the Old Step to Inactive: Update the status of the current step to inactive.
2. Insert a New Version: Insert the new query with an incremented version.

Example:

-- Mark the existing query as inactive
UPDATE metadatatable
SET status = 'inactive', updated_at = CURRENT_TIMESTAMP
WHERE program_name = 'Risk Program' AND product_name = 'Personal Loan' 
  AND stage_name = 'Stage 1' AND step_name = 'New Logic Step' AND status = 'active';

-- Insert the updated query with a new version
INSERT INTO metadatatable (
    program_name, product_name, stage_name, step_name, version, status, operation_type, query, 
    custom_logic, temp_view_name, table_name, write_mode, snapshot_mode, stage_priority, 
    steps_priority, month_year, created_at, updated_at
)
VALUES (
    'Risk Program', 'Personal Loan', 'Stage 1', 'New Logic Step', 2, 'active', 'SQL', 
    'SELECT * FROM transactions WHERE risk_score > 750', 
    NULL, 'filtered_view', 'high_risk_table', 'overwrite', NULL, 1, 3, '202412', CURRENT_TIMESTAMP, CURRENT_TIMESTAMP
);

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c) Rolling Back to a Previous Version

To roll back to a previous version:

1. Set Current Version to Inactive: Mark the current version as inactive.
2. Reactivate the Previous Version: Update the status of the required version to active.

Example:

-- Mark the current version as inactive
UPDATE metadatatable
SET status = 'inactive', updated_at = CURRENT_TIMESTAMP
WHERE program_name = 'Risk Program' AND product_name = 'Personal Loan' 
  AND stage_name = 'Stage 1' AND step_name = 'New Logic Step' AND status = 'active';

-- Reactivate the previous version
UPDATE metadatatable
SET status = 'active', updated_at = CURRENT_TIMESTAMP
WHERE program_name = 'Risk Program' AND product_name = 'Personal Loan' 
  AND stage_name = 'Stage 1' AND step_name = 'New Logic Step' AND version = 1;

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4. Automation of Metadata Updates

a) Python Script for Metadata Updates

Use a script to automate updates to the metadata table.

from pyspark.sql import SparkSession
from datetime import datetime

# Initialize SparkSession
spark = SparkSession.builder 
    .appName("Metadata Update Process") 
    .enableHiveSupport() 
    .getOrCreate()

def update_metadata(metadata_table, changes):
    for change in changes:
        step_name = change["step_name"]
        program_name = change["program_name"]
        product_name = change["product_name"]
        stage_name = change["stage_name"]
        new_query = change["query"]
        new_custom_logic = change.get("custom_logic", None)
        version = change.get("version", None)

        # Mark the current version inactive
        spark.sql(f"""
            UPDATE {metadata_table}
            SET status = 'inactive', updated_at = CURRENT_TIMESTAMP
            WHERE program_name = '{program_name}' AND product_name = '{product_name}' 
              AND stage_name = '{stage_name}' AND step_name = '{step_name}' AND status = 'active'
        """)

        # Insert the new version
        spark.sql(f"""
            INSERT INTO {metadata_table} (
                program_name, product_name, stage_name, step_name, version, status, 
                operation_type, query, custom_logic, temp_view_name, table_name, write_mode, 
                snapshot_mode, stage_priority, steps_priority, month_year, created_at, updated_at
            )
            SELECT 
                '{program_name}', '{product_name}', '{stage_name}', '{step_name}', 
                COALESCE(MAX(version), 0) + 1 AS version, 'active', 
                operation_type, '{new_query}', '{new_custom_logic}', temp_view_name, 
                table_name, write_mode, snapshot_mode, stage_priority, steps_priority, 
                month_year, CURRENT_TIMESTAMP, CURRENT_TIMESTAMP
            FROM {metadata_table}
            WHERE program_name = '{program_name}' AND product_name = '{product_name}' 
              AND stage_name = '{stage_name}' AND step_name = '{step_name}'
        """)

# Example Changes
changes = [
    {
        "program_name": "Risk Program",
        "product_name": "Personal Loan",
        "stage_name": "Stage 1",
        "step_name": "New Logic Step",
        "query": "SELECT * FROM transactions WHERE risk_score > 800",
        "custom_logic": None
    }
]

# Update Metadata Table
update_metadata("metadatatable", changes)

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5. Best Practices

1. Audit Trail:
   • Always retain previous versions for audit and rollback purposes.
2. Validation:
   • Validate all new queries or logic before updating the metadata.
3. Testing:
   • Maintain a test environment to validate metadata changes before promoting them to production.
4. Automation:
   • Use scripts to automate metadata updates, ensuring consistency and reducing manual errors.
5. Change Log:
   • Maintain a separate table or document to log metadata changes, including who made the changes and why.

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This framework ensures transparency, traceability, and rollback capabilities for metadata management in your ETL process.