Analytics Marts → Business Exports

GCP • Analytics • Intermediate • Retail

Analytics

Architecture Diagram

Overview

Analytics data is not always consumed inside BigQuery.

Marketing teams may need customer segments for campaigns. Operations teams may need recent order files. External systems may require periodic exports to keep their data up to date.

In this pipeline, you will build a simple reverse ETL process on GCP.

You will export business-ready datasets from BigQuery, generate files in Cloud Storage, track what was delivered, and maintain an audit trail of every export.

What You Will Build

Export customer and order datasets from BigQuery
Generate CSV and Parquet files for downstream systems
Create reusable export definitions
Deliver business datasets to Cloud Storage
Maintain an audit trail for every export
Track successful and failed deliveries
Archive exported files for future reference
Orchestrate exports using Cloud Composer

Tech Stack

BigQuery • Google Cloud Storage • Cloud Composer Apache Airflow • SQL

Learning Outcomes

After completing this pipeline, you will be able to:

Export business datasets from BigQuery
Generate CSV and Parquet files for downstream consumers
Build reusable export pipelines
Deliver analytics data to external systems
Track export history and delivery status
Archive exported files for future reference
Orchestrate reverse ETL workflows using Cloud Composer
Understand how analytics data is shared outside the warehouse