Merging Real-Time Analytics with Big Data Processing: Data Lake houses

Abstract

Integrating the scope and adaptability of data lakes with the performance and dependability of data warehouses is transforming organizational data management and analysis. Although conventional data lakes are affordable and able to retain large amounts of unstructured, raw data, they usually do not improve the performance, consistency, or governance required for complex analytics. Data warehouses struggle with different data kinds, which results in higher expenses; but, they are adept in managing structured data, therefore enabling fast searches and efficient data management. By combining the benefits of both systems, the lakehouse architecture balances these differences and provides a coherent platform that guarantees excellent performance and consistency while allowing organized, semi-structured, and unstructured data. Eliminating obstacles between data storage and processing helps companies to manage vast volumes of data and conduct real-time analytics on one platform. Dealing with several applications—business intelligence, machine learning, predictive analytics—managing a number of them helps one to handle data and collaborate. Lakehouses enable companies to better manage their data by eliminating extraneous information and streamlining exchanges, therefore reducing expenses and increasing production. The process may be more challenging if one attends to legal and security concerns, monitors building expenses, and makes sure the program runs with current technology.