Objective:
Transform CSV data stored in Azure Blob Storage using Azure Data Factory (ADF) to ensure accuracy and integrity.
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Approach:
Requirement Gathering: Understand the data structure, transformations, and output requirements.
ADF Configuration: Integrate ADF with Blob Storage and set up a modular pipeline for maintainability.
Data Ingestion: Use ADF’s Copy Data activity to load CSV files, applying validations to ensure data integrity.
Data Transformation: Utilize Mapping Data Flows for cleaning, filtering, and aggregating data, incorporating custom logic if needed.
Validation: Implement quality checks and log discrepancies to ensure accurate outputs.
Output Delivery: Write transformed data back to Blob Storage or other systems, automating the pipeline for future use.
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Benefits:
Automation: Minimal manual intervention for recurring transformations.
Scalability: Handles large data volumes efficiently.
Data Integrity: Ensures reliable results through validations.
Timeline: 5-8 days, including configuration, pipeline setup, testing, and deployment.
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Why Me?
With 2+ years of experience as an Azure Data Engineer, I specialize in ADF and Blob Storage solutions, delivering high-quality, scalable data transformations with a focus on accuracy.
I look forward to collaborating and tailoring this solution to your needs.
Best regards,
Abhishek Tiwari
Sr. Data Engineer | Azure Specialist