Streamlining Data Integration and Analytics with Scalable Azure Cloud Solutions
Overview
The objective was to transfer the flight catering data to Azure which has information regarding orders, inventory and customer details:
- Replace sharing of output with business in CSV/ excel format
- Implement Azure Data Factory (ADF), with Azure BLOB storage to enabale scalable data storage, processing and analysis capabilities for their catering operations
- Develop pipeline processes to pull data from diverse sources such as APIs, databases and files for transformation and ingestion into Azure
Solution
Incremental/ Bulk data loading: Transfer only the new or updated data from the source to the destination, reducing processing time and resource usage:
- Use watermarking (based on date, time, or ID columns) to track changes.
- Implement delta tables or change data capture (CDC) techniques.
- Design pipelines to query data where the modified timestamp is greater than the last load time.
- Leverage Azure Blob Storage or ADLS as staging layers.
- Use PolyBase or COPY INTO statements for high-speed transfers
- Optimize partitioning and parallelism to handle large volumes of data
CI/CD Process in ADF: Automate deployment and version control of ADF pipelines.
- Use Git integration to manage ADF pipelines (dev, QA, preprod, prod branches).
- Implement ARM templates to ensure environment consistency.
- Automate deployment with Azure DevOps pipelines or GitHub Actions.
- Use triggers to manage deployments in a phased rollout.
Impact
Developed pipeline processes to pull data from diverse sources such as APIs, databases and files for transformation and ingestion into Azure
Dashboards and reports generated using Power BI
Generated fact tables for transactional or event-based data
