OPTIMIZING DATA QUALITY: USING SSIS FOR DATA CLEANSING AND TRANSFORMATION IN ETL PIPELINES

Abstract

Modern organizations depend on well-designed ETL pipelines for their data integration and data quality routines for analytics and decision making, pulling data from across systems to meet the demands of Data Lake. SQL Server Integration Services (SSIS) offered by Microsoft is a widely used ETL tool with strong data cleansing and transformation capabilities. Data Quality: Preprocessing for data is extremely essential as the precision of the insights provided and operational efficiency largely depend on it. SSIS helps customers address this need by providing components and capabilities to clean, standardize, and validate your data at scale.

Hence, this paper will focus, the methodologies and the best practice to enhance the data quality using SSIS, explaining the works better with real chosen data. SSIS automates data transformations and handles numerous records from various sources, making it crucial for data quality. This leads to more accurate data and easier analytics and reporting across ETL workflows when organizations implement SSIS.

Additionally, it provides empirical analysis and case studies that illustrate how SSIS improves ETL workflows through the maintenance of data accuracy, consistency, and completeness. It highlights the use of SSIS’s built-in tools, including data profiling, fuzzy lookups, and derived columns, to overcome intricate data quality issues. The results reinforce the potential of SSIS to revolutionize the data governance frameworks, fuelling ongoing improvements in data quality.