Azure Data Factory Pipeline Development
Budget: $10 – $30 USD
Project Name:
CSV File Ingestion to Azure SQL using Azure Data Factory
Objective:
To design, develop, and deploy a scalable and secure pipeline using Azure Data Factory (ADF) that ingests .csv files from Azure Blob Storage and loads the data into corresponding Azure SQL Database tables with proper data validation.
Scope of Work
Source System
Location: Azure Blob Storage
File Format: .csv (UTF-8 or specified encoding)
File Structure: May vary; schema and sample files to be provided
Delimiter Handling: Support for custom delimiters (e.g., ~!$!~) if applicable
File Validation: Check for headers, blank files, incorrect delimiters
Target System
Destination: Azure SQL Database
Schema Mapping: Based on finalized Source-to-Target Mapping Document (STTM)
Load Method: Truncate and load, or incremental (based on use case)
ADF Pipeline Requirements
Parameterized ADF pipelines to dynamically ingest multiple files
Support for custom or dynamic folder structure (e.g., dated folders)
Data type conversion and transformation rules
Retry mechanism and failure alerts
Integration with Azure Key Vault for secure credentials
Reusable components (datasets, linked services)
Data Load Validations
Validation Type Description
File Format Check Validate file format (extension, structure, encoding, delimiter)
Schema Validation Ensure incoming file matches expected column structure and data types
Header Validation Confirm presence and correctness of headers
Null Check Reject or flag rows with missing mandatory fields
Row Count Check Compare source file row count with rows inserted into SQL
Duplicate Check Optional de-duplication based on defined business keys
Data Type Validation Check for invalid values (e.g., date strings, decimals in integer columns)
Post-Load Audit Generate success/failure logs and row counts per file
Deliverables
ADF pipelines with parameterization, error handling, and validation logic
Source-to-Target Mapping Document (STTM)
Linked services, datasets, integration runtime config
Post-load validation log mechanism (either SQL table or Blob log files)
Deployment scripts (ARM templates or via GitHub repo)
Basic handoff documentation (pipeline flow, parameter config, troubleshooting guide)
CSV File Ingestion to Azure SQL using Azure Data Factory
Objective:
To design, develop, and deploy a scalable and secure pipeline using Azure Data Factory (ADF) that ingests .csv files from Azure Blob Storage and loads the data into corresponding Azure SQL Database tables with proper data validation.
Scope of Work
Source System
Location: Azure Blob Storage
File Format: .csv (UTF-8 or specified encoding)
File Structure: May vary; schema and sample files to be provided
Delimiter Handling: Support for custom delimiters (e.g., ~!$!~) if applicable
File Validation: Check for headers, blank files, incorrect delimiters
Target System
Destination: Azure SQL Database
Schema Mapping: Based on finalized Source-to-Target Mapping Document (STTM)
Load Method: Truncate and load, or incremental (based on use case)
ADF Pipeline Requirements
Parameterized ADF pipelines to dynamically ingest multiple files
Support for custom or dynamic folder structure (e.g., dated folders)
Data type conversion and transformation rules
Retry mechanism and failure alerts
Integration with Azure Key Vault for secure credentials
Reusable components (datasets, linked services)
Data Load Validations
Validation Type Description
File Format Check Validate file format (extension, structure, encoding, delimiter)
Schema Validation Ensure incoming file matches expected column structure and data types
Header Validation Confirm presence and correctness of headers
Null Check Reject or flag rows with missing mandatory fields
Row Count Check Compare source file row count with rows inserted into SQL
Duplicate Check Optional de-duplication based on defined business keys
Data Type Validation Check for invalid values (e.g., date strings, decimals in integer columns)
Post-Load Audit Generate success/failure logs and row counts per file
Deliverables
ADF pipelines with parameterization, error handling, and validation logic
Source-to-Target Mapping Document (STTM)
Linked services, datasets, integration runtime config
Post-load validation log mechanism (either SQL table or Blob log files)
Deployment scripts (ARM templates or via GitHub repo)
Basic handoff documentation (pipeline flow, parameter config, troubleshooting guide)