Models for building an enterprise data Warehouse

Abstract

We live in an era of big data where data is generated continuously and at an exponential rate, so we need a place to collect, store and process it. Data can be generated by devices and sensors either remotely or in real time. The place where data can be collected, stored and processed is called a data warehouse. The term «data warehouse» was coined in the 1980s to describe a technology that allows companies to integrate data from many different sources into a single model. Data integration was seen as the most promising means of supporting deep insights into operational processes and identifying the latest opportunities to improve efficiency and profitability at the organizational level. An equally important role of data warehouses was seen as structural optimization by eliminating redundant decision support systems, most of which relied in one way or another on organizational performance data from the same centralized source. In contrast, the concept of a single data warehouse promised to eliminate redundancy and ensure data consistency, making it more suitable for optimizing management decisions at the enterprise level. This article discusses the concept of data warehouse, identifies its main advantages, reviews and compares enterprise data warehouse models and data platform on the example of Sberbank.