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GaneshSDM/README.md

ETL Accelerator Demo

This repository packages a lightweight but end-to-end demonstration of an ETL accelerator that can be walked through with business stakeholders. The demo is split into distinct layers so that each capability can be showcased in isolation:

Layer Location Highlights
Orchestration dags/etl_accelerator.py Airflow DAG that chains ingestion, Databricks, and validation tasks
Data Engineering etl/etl_job.py Extract-transform-load workflow running on a sample sales dataset
Remediation remediation/retry_handler.py Shared retry helper used across the stack
Monitoring monitoring/ Prometheus exporter and anomaly detection logic
Infrastructure as Code infrastructure/main.tf Terraform blueprint for Snowflake and Databricks resources
Strategy & Vision docs/ai_cloudops_platform.md AI CloudOps platform blueprint for enterprise multi-cloud operations
Working Prototype cloudops/run_demo.py Executable simulation of the CloudOps control plane with multi-cloud connectors
CloudOps Walkthrough docs/cloudops_getting_started.md Step-by-step instructions for running and extending the prototype

Getting started

  1. Run the ETL pipeline locally

    python -m etl.etl_job

    The run uses the built-in Snowflake stub, writes a CSV to demo_snowflake_output.csv, and logs the number of rows processed.

  2. Preview anomaly detection

    python - <<'PY'
from monitoring.anomaly_detector import detect_anomalies
print(detect_anomalies("etl/sample_sales.csv", "amount"))
PY

  3. Review the orchestration and infrastructure blueprints

    • dags/etl_accelerator.py shows how Airflow wires the components together.
    • infrastructure/main.tf illustrates how Snowflake and Databricks jobs would be provisioned.
    • cloudops/run_demo.py demonstrates the CloudOps control plane concepts in code with sample connectors and recommendations. See docs/cloudops_getting_started.md for a full walkthrough of how to run the simulation and validate it with automated tests.

Monitoring demo

Run the Prometheus exporter to collect metrics every five minutes:

python monitoring/exporter.py

Prometheus can scrape the metrics using monitoring/prometheus.yml.

This project is intentionally self-contained so it can be demoed on a laptop without cloud connectivity while still representing a realistic architecture.

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