Web Analytics Migration Pipeline

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Web Analytics Migration Pipeline

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Analysis

As a Digital Content Team member at the NSW Department of Education, I designed and implemented a comprehensive data pipeline for archiving Universal Analytics data across the education.nsw.gov.au domain.

The Challenge:

  • Need to preserve historical web analytics data before the Universal Analytics sunset
  • Complex data requirements spanning multiple stakeholders and business units
  • Large volume of data requiring efficient processing and storage

    • Solution Architecture:

  • Leveraged Google Cloud Platform for scalable, secure data processing
  • Integrated Supermetrics API for reliable data extraction
  • Implemented BigQuery as the data warehouse solution
  • Connected Looker Studio for intuitive data visualization and analysis

    • Technical Implementation:

  • Designed and optimized ETL processes for efficient data transfer
  • Modeled data warehouse schema based on specific business requirements
  • Implemented data quality checks and validation procedures
  • Created automated workflows for regular data updates

    • Impact:

  • Successfully preserved critical historical analytics data
  • Enabled seamless transition to new analytics platform
  • Improved data accessibility and reporting capabilities
  • Enhanced decision-making through better data visualization

    • Technologies Used

    Google Cloud Platform
    Python
    BigQuery
    Looker Studio
    Supermetrics API
    Universal Analytics
    ETL

    Key Achievements

    • Designed and implemented end-to-end data pipeline in Google Cloud Platform
    • Successfully archived historical Universal Analytics data before sunset
    • Created efficient ETL processes integrating multiple data sources
    • Implemented scalable data warehouse solution using BigQuery
    • Developed intuitive analytics dashboards in Looker Studio