Mastering Data Quality Management in Google Cloud: Strategies for Clean and Reliable Data Pipelines

Tulasiram Yadavalli

Abstract: Data quality is essential for creating clean, reliable data pipelines. In Google Cloud, tools like Dataflow, Cloud Dataprep, and BigQuery help ensure that data is validated, cleansed, and transformed efficiently. These tools are designed to address common challenges in data management, such as incomplete data, duplicates, or incorrect formats. This article discusses best practices for data quality management on Google Cloud, including validation techniques, cleansing strategies, and transformation processes. It looks at how these strategies improve the reliability and usability of data, offering scalable solutions to data integrity issues. With the increasing reliance on data-driven decision-making, mastering data quality management is critical for modern businesses to ensure data consistency and accuracy across their pipelines.

Keywords: Data quality, Google Cloud, Dataflow, Cloud Dataprep, BigQuery, data cleansing, data validation, data transformation, data pipelines, cloud computing