Implementing Data Lakehouse Architecture for Business Data Process Optimization in big Corporations.

Abstract

Large corporations generate significant management challenges by creating increasing volumes of structured and unstructured data from many sources. Traditional data systems can yield fragmented information and inconsistent data quality, so hindering analysis and decision-making. A unified data lakehouse architecture is implemented to optimize corporate data operations in large enterprises to address these limitations.

The data flow is structured in the implementation via a layered medallion architecture including Bronze, Silver, and Gold layers. Raw data from several systems are entered into the Bronze layer and subsequently cleansed, validated, and improved in the silver layer. The Gold layer produces carefully curated, analytics-ready datasets for reporting and decision assistance. Each level employs data validation and transformation procedures to enhance reliability, integrated governance and metadata management provide consistent standards and traceability. The architecture accommodates both batch and streaming data processing to meet various company requirements.

Through rigorous validation, improved governance with centralized oversight, and advanced analytics from a unified data source, the implementation results in higher data quality. Redundant processing is minimized, and data access is streamlined, thereby enabling quicker insights and more agile organizational processes. Consolidating all data into a singular, scalable store enables architects to eliminate barriers and allows teams to focus on deriving strategic insights rather than on data management tasks. Ultimately, the findings indicate that lakehouse architecture provides large enterprises with a scalable, integrated, and cost-effective data management framework, hence enhancing efficient, data-driven decision-making and optimal business operations.