Balaji Adavi

Azure Sas Data Engineer @Premium Retail Services

Mississauga, ON, CA

Involved in building an Enterprise Data Lake utilizing Data Factory and Blob storage, enabling teams to work with complex scenarios and ML solutions.• Created SAS data sets by extracting data from SQL Server using SAS/EG. Used SAS/ACCESS to read datasets from Databases, Flat files, also read and write other PC File formats. • Developed Mappings between Source and Target and loaded the data and Validated Data for missing values based on the requirements. • Developed and optimized ETL pipelines using PySpark, transforming and aggregating large-scale structured and unstructured data from multiple sources.• Developed and maintained ETL pipelines using SAS Base and SAS DI Studio to extract, transform, and load data between Netezza and enterprise data warehouses.• Integrated large volumes of data from IBM Netezza using explicit pass-through SQL queries and SAS/ACCESS engine for optimized performance.• Data ingestion into Azure services like Azure Data Lake, Azure Storage, Azure SQL, and Azure Data Warehouse (AzureDW), and processing data in Azure Databricks.• Analyzed large datasets using SAS, SQL, and Excel to identify cost-saving opportunities.• Designed and automated business intelligence reports in SAS Visual Analytics, reducing manual reporting time.• Created SAS macros for reusable ETL components, improving code maintainability and standardization across Netezza data workflows.• Built and maintained ETL workflows using Azure Data Factory, Data Lake, and Blob Storage, supporting data ingestion, transformation, and orchestration.• Using Linked Services/Datasets/Pipeline/ to extract, transform and load data from various sources such as Azure SQL, Blob storage, Azure SQL Data warehouse, write-back tool, and backwards, ADF pipelines were created.