Description:
Developed cloud-based data pipelines to automate the refresh of platform usage data for business intelligence dashboards. This project leveraged workflow automation tools to orchestrate data extraction from a database, ensuring up-to-date insights into platform utilization and user behavior. The dashboards provide critical business intelligence, enabling data-driven decision-making regarding platform usage.
Key Technologies:
- Python (API interaction)
- SQL (Database interaction)
- Business Intelligence Tool (e.g., Power BI)
- Workflow Automation Tool (e.g., Power Automate)
- Cloud Data Warehouse (e.g., Azure SQL Database, AWS Redshift)
- API Management (e.g., Azure API Management, AWS API Gateway)
- Version Control: Git
Project Overview:
- Data Extraction: Developed Python scripts to extract platform usage data from internal logs and load it into a database.
- Data Transformation and Loading: Implemented SQL queries to transform and load the data into appropriate tables within the database, preparing it for dashboard consumption.
- Dashboard Creation: Designed and developed interactive dashboards to visualize key metrics related to platform usage, including usage trends, user activity, and performance indicators.
- Workflow Automation: Created automated workflows to automate the data refresh process, scheduling regular data extraction and updates to the dashboard datasets.
- Cloud Data Warehouse Integration (If Applicable): If utilizing a cloud data warehouse, ensured seamless data transfer and synchronization between the source database and the cloud data warehouse.
- API Management Integration (If Applicable): If utilizing API management, ensured proper API endpoint management and secure access to data.
- Monitoring and Error Handling: Implemented monitoring and error handling within automated workflows to ensure data integrity and timely notifications in case of failures.
Key Achievements:
- Automated Data Refresh: Eliminated manual data updates for dashboards, ensuring data accuracy and timeliness.
- Improved Data Accuracy: Enhanced data accuracy by automating data extraction and transformation processes, reducing the risk of human error.
- Enhanced Dashboard Performance: Optimized data pipelines and queries to ensure efficient data retrieval and dashboard performance.
- Increased Efficiency: Reduced the time required for data updates and dashboard maintenance, freeing up resources for other tasks.
- Improved Decision-Making: Provided stakeholders with up-to-date and reliable insights into platform usage, enabling data-driven decision-making.
- Scalable Solution: Developed a scalable and maintainable data pipeline that can accommodate future growth in data volume and complexity.
- Cloud Integration: Integrated cloud services, showcasing expertise in cloud-based data solutions.
Project Context:
This project addressed the need for automated and reliable data updates for platform usage dashboards. By automating data extraction, transformation, and refresh processes, this project improved the efficiency and accuracy of data analysis, enabling better decision-making regarding platform utilization.
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