BigQuery Database Creation and Automation
Manuel Navarro
Verified
Algorithm to migrate and automate data from Google Drive to BigQuery
I designed and implemented a data migration and automation solution for my client, streamlining their data pipeline from Google Drive to BigQuery and enabling efficient visualization in Data Studio. The project involved developing two distinct algorithms to handle monthly and weekly data transfers, each tailored to meet specific requirements.
Project Overview:
The solution aimed to automate the ingestion of structured data files (e.g., CSV, Excel) stored in Google Drive, transferring them to BigQuery for storage, processing, and analysis. Once the data was successfully migrated, it was visualized through dynamic dashboards in Data Studio, providing real-time insights for the client.
Developed Algorithms:
Monthly Data Transfer Algorithm:
Automatically detects and processes new monthly data files uploaded to Google Drive.
Transfers data to dedicated BigQuery tables optimized for monthly reports.
Triggers periodic updates in Data Studio, ensuring fresh insights for monthly analysis.
Validates and cleanses data to ensure consistency and accuracy before ingestion.
Weekly Data Transfer Algorithm:
Designed for handling more frequent data uploads with a similar automated detection mechanism.
Processes and validates weekly data files from Google Drive.
Appends and merges data into relevant BigQuery tables for cumulative analysis.
Provides real-time visualizations in Data Studio, enhancing operational monitoring.
Notebooks added to BigQuery for automation
Technologies & Tools Used:
Python & Google Apps Script: To automate file detection, validation, and ingestion processes.
BigQuery APIs: For seamless data storage, transformation, and querying.
Data Studio: For creating dynamic dashboards with customized metrics and visuals.
Key Results & Achievements:
š End-to-End automation: Eliminated the need for manual uploads and processing by implementing fully automated data ingestion pipelines.
ā³ Significant time savings: Reduced data processing time from a few hours to just 10 minutes.
š Improved data consistency and accuracy: Through validation checks and standardized ingestion processes.
š Enhanced data accessibility: Integrated Data Studio dashboards provided real-time insights, improving decision-making.
š Scalability and reliability: The solution is adaptable to accommodate additional data sources or higher data volumes as needed.
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Posted Mar 17, 2025
ā³ Time savings: Reduced data processing time from a few hours to 10 minutes. š Data validation checks and standardized processes. š End-to-End automation
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4
Views
103
Timeline
Dec 5, 2024 - Dec 20, 2024
Clients
Tupia
