Web Data - GA4
Tools & Technologies
ETL Techniques: Extract, Transform, Load Data Storage: Microsoft Azure Blob Storage, Google Cloud Storage
Data Warehouse: Snowflake, BigQuery
Analytics Server: GA4
Visualization: Tableau
Languages: Python, SQL
Situation
The transition from Google's Universal Analytics to GA4 raised concerns within the company's digital marketing team, as continuity of critical web analytics data was essential. The objective was to migrate important historical data from Universal Analytics and integrate new GA4 data into the central data warehouse. This integration was vital for consolidating web analytics with internal systems, enabling comprehensive tracking of traffic sources and their impact on sales.
Task
The key objectives were:
Migrate valuable data from Universal Analytics, ensuring no loss of critical historical data during the transition.
Integrate GA4 data into the data warehouse to enable a unified web and sales data view.
Seamlessly merge data from multiple sources (such as cloud storage platforms) into the data warehouse while maintaining data integrity and accommodating the evolving nature of GA4 data structures.
Action
The project involved the following steps:
Data Migration from Universal Analytics:
Data Extraction: Historical data was exported in CSV format and stored in a secure cloud storage solution.
Data Loading: The exported data was imported into Snowflake after appropriate table structures were created to preserve the historical data.
GA4 Data Integration:
Schema Management: A process was developed to handle the evolving GA4 table structures. This included extracting table schemas from BigQuery, creating corresponding tables in Snowflake, and managing schema changes over time.
Data Pipeline Development: GA4 data, stored in JSON format in cloud storage, was transformed using Python scripts and scheduled queries. These prepared the data for smooth integration into Snowflake.
Data Staging & Integration: The transformed GA4 data was staged and loaded into Snowflake using stored procedures to ensure accurate and efficient loading into raw and master tables.
Data Consolidation and Visualization:
The GA4 data was linked with internal systems, enabling detailed tracking of sales channels, whether from web traffic or other sources.
A Tableau dashboard was created, providing actionable insights into the effectiveness of marketing efforts.
Result
The project successfully transitioned seamlessly from Universal Analytics to GA4, with all critical data being migrated and integrated into Snowflake. The new, centralized data repository enabled a comprehensive analysis of marketing efforts and their direct impact on sales performance. Key outcomes included:
Enhanced Tracking: Detailed tracking of sales back to their traffic sources, helping identify the most effective marketing channels.
Operational Efficiency: Automated data pipelines reduced manual data processing, allowing for real-time insights and quicker decision-making.
Strategic Insights: A Tableau dashboard offered clear insights into campaign performance, optimizing future marketing strategies
Challenges & Solutions
Challenge: The evolving nature of GA4 data and limited documentation made it difficult to understand and map data structures between GA4 and BigQuery.
Solution: Data quality checks and adaptable integration processes were developed to manage ongoing schema changes and ensure reliable data processing.
Impact & Contributions
The successful integration and visualization of GA4 data provided the marketing team with crucial insights into the performance of different traffic sources and their contribution to sales. This project ensured the continuity of data during a major platform transition and empowered the team with data-driven insights, improving their strategic planning and execution.
Conclusion
This project demonstrates expertise in managing complex data migrations and integrations using modern tools and methodologies. It highlights adaptability in evolving data environments while ensuring data integrity and business value—essential skills in data engineering.
Visuals & Samples & Link to Full Project
Visuals and links are unavailable due to confidentiality and the internal nature of the data.
