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A data warehouse is an environment that combines an integrated decision support database with software to perform extraction transformation loading processes from a variety of operational and external sources. These technologies are combined to support historical, analytical, and business intelligence (BI) requirements.
A business intelligence (BI) process collects, stores, and analyzes the data produced by a company’s activities. BI encompasses data mining, process analysis, performance benchmarking, and descriptive analytics. BI produces reports, analysis, and other supports analytical best practices.
Explaining data warehouse automation can be challenging, but all data warehouse / business intelligence professionals should understand the concepts and practices. Data warehouse automation is
Three steps can help any organization develop the right architecture for business intelligence, analytics, or a re-design of a data warehouse The best place to
Successful re-architecture of data warehousing / business intelligence / analytics environments pose business challenges in addition to information technology issues Many large corporations across a
Rather than apply new words or phrases to poor data architecture concepts, learn how a data strategy should influence effective data architecture, especially for analytics
All successful business intelligence / analytics endeavors are based on a formal strategy and use a best-practices based methodology, even in agile environments. Many companies
Using sustainable approaches in information technologies, especially with business intelligence and analytics initiatives, can help organizations improve the planet and their profits Sustainability is a
Applying DevOps-style test automation to projects can guarantee a high level of data quality in any business intelligence / analytics initiative. According to a CIO.com article,
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
Preparing a data warehouse testing strategy can ensure the successful development and completion of end-to-end testing of any data warehouse, data mart, or analytical environment.
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
Today’s digital world and fast paced business environment demands pervasive, timely analytics. Organizations must deliver speed, agility and ease of use for data access and
Investing in new sources of data can help to unravel complex customer behavior and improve key analytical insights Introduction Big Data and Analytics are all
