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Creating an ETL Data Pipeline on Google Cloud with Cloud Data Fusion & Airflow
Explore the magic of building an ETL pipeline in Google Cloud with this comprehensive tutorial. Learn how to craft a seamless process for extracting, transforming, and loading data into BigQuery, then visualize it effortlessly in Looker Studio.

Step 1: Begin by extracting dummy employee data using the Python Faker library, seamlessly storing it in a designated Google Cloud Storage (GCS) bucket.

Step 2: Dive into the creation of a Cloud Fusion instance, setting up the groundwork for your data pipeline journey.

Step 3: Unveil the magic of Data Fusion as you craft a robust pipeline. Witness the transformation of data while ensuring sensitive information remains masked, ultimately loading it into BigQuery for further analysis.

Step 4: Elevate your data visualization game as you harness the power of Looker Studio, bringing your insights to life in a visually compelling manner.

Join me on this illuminating journey through the intricacies of ETL pipelines, empowering you to master data orchestration and visualization in the Google Cloud ecosystem.

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