As organizations increasingly depend on large-scale data for strategic decision-making, managing data warehouses has become a complex and resource-intensive challenge. This paper introduces DataBay, a unified platform designed to automate the entire data warehouse lifecycle, from data ingestion and transformation to real-time processing, monitoring, and ensuring data quality. By streamlining these processes, DataBay reduces the need for specialized technical expertise, enabling faster implementation and more efficient data management. The platform integrates critical components such as Change Data Capture (CDC), Kafka, and Prometheus to ensure highperformance data processing and real-time monitoring without disrupting production environments. DataBay leverages Avro for data serialization, providing optimal throughput and storage efficiency compared to traditional formats like JSON. Additionally, its automated data pipeline orchestration, along with built-in data quality checks, enhances the reliability and accuracy of insights derived from the data. The platform's architecture is highly scalable, supporting enterprise-level datasets and adapting to evolving business needs. Through its seamless integration and flexibility, DataBay helps businesses make timely, data-driven decisions and enables continuous optimization of data workflows. This paper discusses the platform's architecture, its implementation in real-world industry settings, and the significant business value it delivers by enhancing operational efficiency and empowering data-driven decision-making across organizations.