All Scripts & Code

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This page provides a comprehensive overview of all implementation code and pseudocode for the data platform solution.

Scripts repository: Scripts under tasks/ (ETL, SQL, CI/CD, Terraform) are available in a Gitea repository. See Repository & scripts (Gitea) for the folder map and links to each location.

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ETL Ingestion & Transformation

Implementation Code

Location: ETL Implementation Code

The main ETL orchestration script that reads CSV from Bronze layer, validates data, and writes Parquet to Silver layer. Includes:

• Pandas: src/etl/ingest_transactions.py (Lambda path)
• PySpark: src/etl/ingest_transactions_spark.py (Glue path)
• Supporting modules: s3_operations.py, validator.py, loop_prevention.py, metadata.py, metrics.py, config.py (and *_spark.py variants)
• Scripts: scripts/cleanup_condemned_corrupt.py; optional scripts/bedrock_quality_descriptions.py (see BEDROCK_IMPLEMENTATIONS)

GenAI: Bedrock is used for quarantine explanations, catalog descriptions, report narrative, and SQL docs; additional opportunities are in GenAI in the Ohpen Case & Opportunities.

Pseudocode

Location: ETL Flow & Pseudocode

High-level pseudocode representation of the ETL pipeline flow:

PROCEDURE ProcessTransactions();
BEGIN
    { 1. Read CSV from S3 (Bronze Layer) }
    raw_data := ReadCSVFromS3(bronze_layer_path);

    { 2. Enrich Metadata }
    enriched_data := EnrichMetadata(raw_data);
    { Add row_hash, source_file_id, attempt_count, ingestion_timestamp to each row }

    { 3. Loop Prevention }
    condemned_rows := CheckLoopPrevention(enriched_data);
    { Check for duplicate transactions, previously failed rows, and rows exceeding retry limits }
    { Auto-condemn any matching rows (no retries) }

    { 4. Validate Rows }
    validation_result := ValidateRows(enriched_data);
    { Check schema, required fields, currency codes, data types, and timestamps }
    { Flag invalid rows for quarantine }

    { 5. Circuit Breaker Check }
    IF CircuitBreakerThresholdExceeded() THEN
        BEGIN
            HaltPipeline('Too many errors detected - human intervention required');
            EXIT;
        END
    ELSE
        BEGIN
            { Continue processing }
        END;

    { 6. Transform Data }
    transformed_data := TransformData(validation_result.valid_rows);
    { Add partition columns (year, month) from transaction timestamps }

    { 7. Split Data }
    SplitData(transformed_data, valid_rows, invalid_rows, condemned_rows);
    { Separate into valid (Silver), invalid (quarantine), condemned (no retries) }

    { 8. Write Output }
    WriteToSilverLayer(valid_rows); { Partitioned Parquet }
    WriteToQuarantine(invalid_rows); { With error details }
    WriteToCondemned(condemned_rows); { No retries }

    { 9. Write Success Marker }
    WriteSuccessMarker(run_id, metrics);
END;

Full pseudocode: ETL Flow; full query rationale: SQL Breakdown. Related: ETL Flow & Pseudocode, Assumptions and Edge Cases.

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Data Lake Architecture Design

No implementation code - This task focuses on architecture design and documentation.

Documentation:

• Data Lake Architecture - Complete architecture design covering all layers (Bronze/Silver/Gold + Quarantine + Condemned)

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Analytical Querying & SQL

Implementation Code

Location: SQL Implementation Code

The SQL query implementation for account balance history:

• Main Query: balance_history_2024_q1.sql - Month-end balance history for Q1 2024
• Schema: schema.sql - Schema definition for the transactions table

Query Explanation (Pseudocode-like)

Location: SQL Breakdown

The query implements the Silver → Gold aggregation pattern (1:N relationship). The query logic can be understood as:

1. Generate Account×Month Spine: Create all combinations of accounts and months
2. Left Join Transactions: Join transaction data, preserving NULLs for missing months
3. Calculate Running Balance: Compute cumulative balance per account
4. Filter Month-End: Select only the last transaction per account per month
5. Output: Account ID, Month, and Balance at month-end

Query Structure:

WITH account_month_spine AS (
    -- Generate all account×month combinations
),
transactions_with_balance AS (
    -- Calculate running balance
),
month_end_balances AS (
    -- Filter to month-end snapshots
)
SELECT
    account_id,
    month,
    balance
FROM month_end_balances
ORDER BY account_id, month;

Related: SQL Breakdown, Data Lake Architecture.

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DevOps & CI/CD Automation

Implementation Code

Location: CI/CD Artifacts

Complete CI/CD workflow and infrastructure as code:

GitHub Actions CI/CD Workflows

CI (.github/workflows/ci.yml): Validates and stages on every pull request and push to main; does not deploy.

• Linting and code quality checks
• Comprehensive validation and quality assurance
• Build and test packaged ETL; upload to staging; write _STAGING.json
• Terraform plan only (no apply)

CD (.github/workflows/cd.yml): Builds artifacts and deploys after merge to main. Optional: add manual approval by creating a GitHub environment (e.g. production) with required reviewers and setting environment: production on the deploy job.

• Build and upload ETL scripts to s3://ohpen-artifacts/scripts/ and versioned releases
• Terraform init (S3 backend), plan, and apply (after environment approval)
• Updates Glue jobs and infrastructure

Terraform Infrastructure

File: infra/terraform/main.tf

The Terraform configuration that provisions AWS infrastructure:

• S3 buckets: ohpen-bronze, ohpen-silver, ohpen-gold, ohpen-quarantine, ohpen-artifacts
• IAM roles and policies
• CloudWatch metrics and alarms
• Glue Data Catalog tables

Related: CI/CD Workflow, Data Lake Architecture.

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Summary

Area	Code	Pseudocode	Documentation
ETL Pipeline	ETL Code	ETL Pseudocode	ETL Flow, Assumptions
Architecture	N/A	N/A	Architecture Design
SQL Solution	SQL Implementation Code	SQL Explanation	SQL Breakdown
DevOps	CI/CD Artifacts	N/A	CI/CD Workflow

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Additional Resources

For complete implementation details including all supporting modules, diagrams, and comprehensive examples, see:

• ETL Complete Reference - All ETL diagrams, pseudocode, and implementation code
• SQL Complete Reference - All SQL diagrams, code, and examples
• CI/CD Complete Reference - Complete CI/CD workflow details
• Data Lake Architecture Reference - Complete architecture documentation

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See also

• Executive Summary - Business case and solution overview
• ETL Flow - ETL pipeline design and pseudocode
• SQL Breakdown - SQL query design and optimization
• CI/CD Workflow - Deployment pipeline design
• ETL Scripts - ETL implementation code