Orchestrate a BigQuery SQL Transformation Pipeline

Transform raw retail data in BigQuery into clean, join-ready analytics tables using SQL transformations, stored procedures, and Cloud Composer.
GCP • Lakehouse • Intermediate • Retail

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What this project solves

Raw data is useful, but it is rarely ready for analytics.

Customer data may need standardization, product data may need category and brand enrichment, and order data may need to be converted into current-state and history tables.

In this pipeline, you will transform raw retail tables in BigQuery into refined analytics tables. You will clean and enrich the data using SQL and BigQuery stored procedures, track pipeline state with watermarks, and orchestrate the transformations using Cloud Composer.

What you’ll build

  • Create refined BigQuery tables for retail analytics
  • Standardize customer and address data
  • Build product, category, brand, and variant tables
  • Enrich product variants with brand and category details
  • Build current-state and history tables for orders
  • Create join-ready order item tables
  • Track pipeline state and watermarks

How you’ll build it

  • Read raw retail tables from BigQuery
  • Write SQL transformations for customer, product, and order data
  • Use BigQuery stored procedures to create refined tables
  • Track watermarks so only new or changed data is processed
  • Orchestrate the transformation workflow using Cloud Composer

Tools you’ll use

BigQuery • BigQuery Stored Procedures • Cloud Composer • Apache Airflow • SQL

What you’ll walk away with

After completing this pipeline, you will be able to:

  1. Explain how raw data becomes refined analytics tables
  2. Transform raw BigQuery tables using SQL and stored procedures
  3. Standardize customer, address, product, and order data
  4. Create join-ready tables for downstream analytics
  5. Track watermarks and orchestrate transformations using Cloud Composer

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