Copyright: © the author(s), publisher and licensee Technoscience Academy. This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non- commercial use, distribution, and reproduction in any medium, provided the original work is properly cited International Journal of Scientific Research in Computer Science, Engineering and Information Technology ISSN : 2456-3307 (www.ijsrcseit.com) doi : https://doi.org/10.32628/CSEIT239015 25 A Review on Data Warehousing Concepts, Challenges and Applications Dr. L. C. Manikandan *1 , Dr. R. K. Selvakumar 2 * 1 CSE, Valia Koonambaikulathamma College of Engineering & Technology, Trivandrum, Kerala, India 2 CSE, CVR College of Engineering, Hyderabad, Telungana, India Article Info Publication Issue : Volume 9, Issue 1 January-February-2023 Page Number : 25-31 Article History Accepted: 01 Jan 2023 Published: 15 Jan 2023 ABSTRACT Data warehousing (DW) is a technique for gathering and organizing data from many sources to produce valuable business insights. Business executives can methodically organize, comprehend, and apply their data by adopting data warehousing, which offers structures and tools. This paper's goal is to introduce new learners to the fundamental ideas of DW, as well as its challenges and applications. Keywords: Data Warehousing, OLAP Server, OLE-DB, ODBC, ROLAP, JDBC, MOLAP I. INTRODUCTION A database system created for analytics is called a data warehouse. Data warehouse is frequently used to connect and analyse corporate data from many sources [1]. The central component of the Business Intelligence system, which is designed for data processing and reporting, is the data warehouse. Data cleansing, integration, and consolidation are necessary for the creation of a data warehouse. The following discussion covers important data warehouse characteristics [5]. Subject-oriented: Instead of focusing on the organization's regular operations, it offers information on a certain topic. Products, clients, suppliers, sales, revenue, and other topics are all possible. Integrated: Data from various sources, including relational databases, flat files, etc., are combined to create the data warehouse. The effective analysis of data is improved by this integration. Time Variant: A specific time period is assigned to the data collected in a data warehouse. Data in a data warehouse offers information from a historical perspective. Non-volatile: Non-volatile data does not lose previous information when new information is added to it. Because the operational database and the data warehouse are maintained apart, frequent changes in the operational database do not affect the data warehouse. II. THREE TIER DATA WAREHOUSE ARCHITECTURE Typical three-tier architectures [5, 11] for data warehouses include a bottom tier (data warehouse server), middle tier (OLAP server) and top tier (Front end Tools) shown in Figure1.