Applying DevOps-style test automation to projects can guarantee a high level of data quality in any business intelligence / analytics initiative. Introduction According to a CIO.com article, Gartner Group recently stated that between 70 and 80 percent of business intelligence initiatives are initially considered failures but many resume due to their importance to the organization. As businesses create (and need) more data than
Data extraction, transformation, and loading processes enable many activities in information technology projects. Understanding the concepts and practices of ETL is essential for all data and technology professionals Introduction Data extract, transform, load (ETL) is a process of copying data from one or more sources into a target system which is usually designed to represent the data differently from the
An Overview of Data Warehouse Testing Data warehouse and data integration testing should focus on ETL processes, BI engines, and applications that rely on data from the data warehouse and data marts. Introduction There is little that casts doubt on a data warehouse and BI project more quickly than incorrectly reported data. It is crucial that data warehouse project teams do all in their power to deliver reports with high
Data warehouse implementations are vulnerable to internal as well as external security threats. Follow these mitigating steps to reduce the risks. Introduction Security threats exist against your information resources, whether the systems are accessible through the Internet or buried deep within your internal network, available only to authorized users, including the enterprise data warehouse and related analytics systems. The increasing
