Business Challenge

• Generic ETL Pipeline for Varied Reporting Periods:
  The API’s different report formats required a pipeline capable of handling both weekly and monthly data loads.
• Data Quality and Restatement:
  Ensuring high data quality and enabling the ability to restate data in case of errors were critical for maintaining accuracy and integrity.
• Scalability for Future Reports:
  The pipeline needed to accommodate different report types beyond the “BrandAnalyticsSearchTerms,” ensuring flexibility for future data requirements.
• Data Validation and Reconciliation (DVR):
  Implementing robust data consistency checks across multiple layers was essential, along with a process to allow corrections for specific accounts or dates.
• Efficient Data Transformation and Storage:
  Transforming and storing large volumes of JSON data (up to 12 million items) required scalable, high-performance solutions in Azure.

Solution

To address these challenges, a highly efficient and scalable ETL pipeline was developed with the following features:

1. Generic and Flexible Design:
   A generic pipeline was built using Azure Data Factory and Azure Databricks to process weekly and monthly data loads.
2. Delta Lake for Data Storage:
   Delta Lake tables were used to store transformed data, enabling delta load patterns for Search_Terms fact and dimension tables.
3. Scheduled Automation:
   Scheduled triggers ensured the pipeline ran automatically—weekly on Mondays at 11 PM and monthly on the 3rd of each month at 11 PM.
4. Data Validation and Reconciliation (DVR):
   A DVR process validated data accuracy across all layers and supported restating data when needed.
5. Advanced Data Transformation:
   Complex transformations were executed in Azure Databricks to prepare the data for analytics.
6. Scalability for Future Needs:
   The pipeline’s flexible architecture supports additional report types and future scalability.

Benefits

1. Actionable Insights:
   Customers could monitor search rank trends and adjust marketing strategies based on consumer interest.
2. Optimized Marketing Efforts:
   Identifying top ASINs by search terms helped focus marketing on high-performing products, optimizing ad spend and improving product listings.
3. Enhanced Branding:
   Tracking unique branded search terms provided insights into brand recognition and effectiveness of branding efforts.
4. Improved ROI:
   Analyzing the correlation between search rank and ad spend enabled efficient budget allocation for maximum return on investment.
5. Category-Specific Strategies:
   Segmenting search terms by category allowed for tailored marketing efforts, improving sales and market penetration.