Advanced Techniques of Data Warehousing for Business Intelligence Using State-of-the-Art ETL Tool

Data warehouse is a physical repository where relational data are organized to provide enterprise wise, cleansed data in a standardized format. In Data Warehouse (DW) environment, Extraction- Transformation-Loading (ETL) processes constitute the integration layer which aims to pull data from data sources to targets, via a set of transformations. ETL is responsible for the extraction of data, their cleaning, conforming and loading into the target. Data warehouses are traditionally refreshed in a periodic manner, most often on a daily basis. Thus, there is some delay between a business transaction and its appearance in the data warehouse. The most recent data is trapped in the operational sources where it is unavailable for analysis. The intention of this survey is to present the research work in the field of ETL technology in a structured way to create and maintain a Data Warehouse. The paper shows that (1) the conceptual and logical modeling of ETL processes, along with some design methods (2) review of open source and commercial ETL tools, along with some ETL prototypes coming from academic world. (3) Proposes to avoid refreshment anomalies through various ETL Tools (like Informatica, Talend etc.),which will help to accelerate the pace of development for future data marts.